ISCA - International Speech
Communication Association


ISCApad Archive  »  2019  »  ISCApad #247  »  Jobs

ISCApad #247

Friday, January 18, 2019 by Chris Wellekens

6 Jobs
6-1(2018-07-08) 2-year post-doc in ASR for low-resource languages, Delft University, The Netherlands

Job opening: 2-year post-doc in ASR for low-resource languages

We are looking for a highly motived post-doctoral researcher in the area of automatic
speech recognition (ASR) for low-resource languages, as part of the newly started
?Human-inspired automatic speech recognition? lab of Dr. Odette Scharenborg at the
Technical University of Delft, The Netherlands.

This project concerns building ASR systems for low-resource languages using linguistic
knowledge. The project aims to investigate different learning and training strategies
(e.g., semi- vs. unsupervised learning, multi-task learning) and architectures of deep
neural networks (DNNs) for the task of low-resource ASR. An important focus of the
project is on the role of linguistic information and multi-linguality in building ASR
systems for low-resource languages. A second important aspect of the project is opening
the DNN ?Black box? by investigating the speech representations in the hidden layers of
the DNNs using visualization techniques, and subsequently using this information to
improve the ASR systems.

We are looking for a highly motivated individual with a strong background in:
-        Deep neural networks
-        Automatic speech recognition

Who preferably has knowledge of one or more of the following topics:
-        Visualization of DNNs
-        Different DNN architectures and training techniques
-        Semi-/unsupervised learning
-        Speech acoustics

Who has/is:
-        A PhD in Computer Science, Electrical Engineering, Computational Linguistics,
Artificial Intelligence or a related field
-        A strong analytical mind
-        Excellent verbal and written communicative skills in English
-        At least 2 journal published journal papers in high-impact journals or conferences as
first author
-        A strong team-worker

We offer a 2-year post-doctoral position in the Multimedia Computing Group, Department of
Intelligent Systems, Faculty of Electrical Engineering, Mathematics, and Computer
Science, Delft University of Technology, the Netherlands.

For inquiries, please contact Dr. Odette Scharenborg (o.e.scharenborg@tudelft.nl).
Applications should be send to o.e.scharenborg@tudelft.nl before August 13, 2018, and
should include:
-        CV
-        Motivation letter
-        List of publications
-        Names and addresses of three referees.

The estimated starting date is October 1, 2018 or as soon as possible after that.
Interviews will likely be held in the week of August 20-24, 2018.

Dr. Odette Scharenborg
Associate Professor and Delft Technology Fellow
Multimedia Computing Group, Faculty of Electrical Engineering, Mathematics, and Computer
Science
Delft University of Technology
The Netherlands

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6-2(2018-07-09) Two academic positions at NTNU,Norwegian University of Science and Technology, Trondheim, Norway

Professor/Associate Professor in Statistical Machine Learning for Speech Technology at NTNU, Norwegian University of Science and Technology, Trondheim, Norway

Faculty position targeting machine learning for pattern recognition, with particular emphasis on applications in speech and language technology at the Signal Processing Group, NTNU. Application deadline is Aug. 31, 2018. See https://www.jobbnorge.no/en/available-jobs/job/154954/professor-associate-professor-in-statistical-machine-learning-for-speech-technology-ie-138-2018 for further information.

 

 

Professor/Associate Professor in Statistical Machine Learning for Signal Processing at NTNU, Norwegian University of Science and Technology, Trondheim, Norway

Faculty position targeting machine learning for analysis, classification, prediction and data mining of (large amounts of) sensor data, typically measurements in time and/or space at the Signal Processing Group, NTNU. Application deadline is Aug. 31, 2018. See https://www.jobbnorge.no/en/available-jobs/job/154952/professor-associate-professor-in-statistical-machine-learning-for-signal-processing-ie-137-2018  for further information.

 

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6-3(2018-07-19) CDD Ingénieur à l'IRISA, Rennes, Bretagne, France

L'équipe Expression de l'IRISA recrute un ingénieur pour un CDD de 24 mois sur le déploiement sur mobile d'un système de synthèse de la parole.

- Descriptif de l'offre en pièce jointe et ici : https://www-expression.irisa.fr/files/2018/07/fiche_de_poste.pdf

- Plus de détails sur l'équipe Expression : http://www-expression.irisa.fr/fr/

- Plus de détails sur l'IRISA : http://www.irisa.fr/

- Date limite de candidature : 10 septembre 2018

 

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6-4(2018-07-20) Post Doc au Laboratoire national de métrologie et d'essais, Trappes (78), France

POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique

Localisation : Laboratoire national de métrologie et d'essais, Trappes (78)

REF : ML/VOX/DE

CONTEXTE :

Le projet ANR VoxCrim (2017-2021) propose d’objectiver scientifiquement les possibilités de mise en œuvre d’une comparaison de voix dans le domaine criminalistique. Deux objectifs principaux : a) mettre en place une méthodologie permettant d’assurer l’efficacité et la compétence des laboratoires réalisant des comparaisons de voix, b) établir des standards de mesures objectives.
Il est nécessaire que les outils et méthodologies utilisés dans la comparaison de voix soient évalués, et que leur utilisation soit effectuée dans un cadre explicable et transparent. Les actions menées dans le projet permettront ainsi de faciliter le traitement d’une comparaison de voix dans les services de police et permettront de renforcer la recevabilité de la preuve auprès des tribunaux.
Le laboratoire national de métrologie et d’essais (LNE) apporte au projet son expertise en métrologie, normalisation, accréditation et comparaison inter-laboratoire, dans le but de constituer une solution méthodologique pratique permettant de rendre le processus de comparaison de voix transparent et explicable.

MISSIONS :

Les missions confiées s’organisent en trois tâches :
-        Spécifications du protocole de validation des méthodes de comparaison de voix, plus spécifiquement dans le domaine de la criminalistique. En s’appuyant sur l’existant en termes de normes et méthodologies de référence, le (la) post-doctorant(e) identifiera les besoins et possibilités pour la mise en place d’un protocole de référence.
-        Le (la) post-doctorant(e) vérifiera l’adéquation du protocole identifié avec les métriques de comparaison de voix identifiées par les chercheurs des laboratoires d’informatique et de phonétique associés au projet. Il (elle) s’assurera également de la compatibilité du protocole avec les méthodes de travail des centres scientifiques de la Police et de la Gendarmerie, membres du projet.
-        Il (elle) collaborera à l’organisation d’une comparaison inter-laboratoire s’appuyant sur ce protocole.

Le (la) post-doctorant(e) bénéficiera du soutien de différentes équipes du LNE dans la menée de ses travaux (équipes évaluation des systèmes de traitement de l’information,  mathématiques-statistiques, et métrologie), et sera en interaction régulière avec les autres laboratoires et centres scientifiques membres du projet.
Des publications (et présentations, le cas échéant) en conférences et journaux internationaux sont attendues du (de la) post-doctorant(e).

Bibliographie : Bonastre, J. F., Kahn, J., Rossato, S., & Ajili, M. (2015). Forensic speaker recognition: Mirages and reality. In Speech Production and Perception: Speaker-Specific Behavior. hal-01473992.

DUREE :

18 mois. Début en janvier 2019.

PROFIL :

Vous êtes titulaire d’un doctorat en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la parole.
Vous possédez des connaissances en méthodologie d’évaluation et en biométrique vocale.

Pour candidater, merci d’envoyer votre CV et lettre de motivation à l’adresse recrut@lne.fr en rappelant la référence : ML/VOX/DE

====================================================
Agnes Delaborde, PhD
Ingénieur de recherche en évaluation IA & robotique
(Research engineer in AI and robotics evaluation)
Direction des essais – DE536
agnes.delaborde@lne.fr
Tél. : +33 (0)1 30 69 11 50 - Mob. : +33 (0)6 26 72 69 80



Laboratoire national de métrologie et d'essais
29 avenue Roger Hennequin 78197 Trappes Cedex - lne.fr

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6-5(2018-08-03) Doctoral thesis at IJLRA (Sorbonne Université) Paris France

Doctoral thesis

Navigation aid for the visually impaired:
Virtual Reality acoustic simulations for interior navigation preparation

 

Laboratories                     IJLRA (Institut Jean le Rond d?Alembert, UMR 7190 CNRS ? Sorbonne Université) and IRCAM (Institut de Recherche et Coordination Acoustique/Musique, UMR 9912 STMS IRCAM ? CNRS ? Sorbonne Université)

Doctoral school               École Doctorale Sciences Mécaniques, Acoustique, Électronique et Robotique (SMAER): ED 391

Discipline                           Acoustics (Virtual Reality, Audio, Interaction, Aide Handicap)

Co-supervision                 Brian KATZ (DR-CNRS, IJLRA) et Markus NOISTERNIG (CR, IRCAM)

Keywords                           Virtual reality, 3D audio, spatial sound, spatial cognition, room acoustics, visual impairments, navigation aid

 

Research context            This thesis project is placed in the context of the ANR 2018-2021 project RASPUTIN (Room Acoustic Simulations for Perceptually Realistic Uses in Real-Time Immersive and Navigation Experiences). In the domains of sound synthesis and virtual reality (VR), much effort had been placed on the quality and realism of sound source renderings, from text-to-speech to musical instruments to engine noise for use in driving and flight simulators. The same degree of effort cannot be seen with regards to the spatial aspects of sound synthesis and virtual reality, particularly with respect to the acoustics of the surrounding environment. Room acoustic simulation algorithms have for decades been improving in their ability to predict acoustic measurement metrics like reverberation time from geometrical acoustic models, at a cost of higher and higher computational requirements. However, it is only recently that the perceptual quality of these simulations are being explored beyond their musical applications. In real-time systems, where sound source, listener, and room architecture can vary in unpredicted ways, investigation of the perceptual quality or realism has been hindered by necessary simplifications to algorithms. This project aims to improve real-time simulation quality towards perceptual realism.

The capability of a real-time acoustic simulation to provide meaningful information to a visually impaired user through a virtual reality exploration is the focus of the project. As a preparatory tool prior to visiting a public building or museum, the virtual exploration will improve user's knowledge of the space and navigation confidence during their on-site visit, as compared to traditional methods such as tactile maps.

The thesis work entails participating in the creation and evaluation of a training system application for visually impaired individuals. Tasks involve the development of an experimental prototype in collaboration with project partners with a simplified user interface for the construction of virtual environments to explore. Working in conjunction with a selected user group panel who will remain engaged in the project for the duration, several test cases of interest will be identified for integration into the prototype and subsequent evaluations. The prototype will be developed by the thesis student in collaboration with Novelab (audio gaming) and IRCAM/STMS-CNRS (developers of the audio rendering engine). Design and evaluation will be carried out in collaboration with the Centre de Psychiatrie et Neurosciences and StreetLab/Institut de la Vision. The ability to communicate in French would be beneficial, but is not mandatory at the start of the project.

Evaluations will involve different experimental protocols in order to assess the accuracy of the mental representation of the learned environments. From the point of view of the metrics relation preservation, participants will have to carry out experimental spatial memory tests as well as onsite navigation tasks.

 

Candidate profile:           We are looking for dynamic, creative, and motivated candidates with scientific curiosity, strong problem solving skills, the ability to work both independently and in a team environment, and the desire to push their knowledge limits and areas of confidence to new domains. The candidate should have a Master in Computer Science, Acoustics, Architectural Acoustics, Multimodal Interfaces, or Audio Signal Processing. A strong interest in spatial audio, room acoustics, and working with the visually impaired is necessary.   It is not expected that a candidate will have already all the skills necessary for this multidisciplinary subject, so a willingness and ability to rapidly step into new domains, including spatial cognition and psychoacoustics will be appreciated.

 

Domaine                            Réalité virtuelle, Audio, Interaction, Aide Handicap

 

Dates                                  Preferred starting date from 1-Nov-2018 to 20-Dec-2019, and no later than March-2019.

 

Application                        Interested candidates should send a CV, transcript of Master?s degree courses, a cover letter (limit 2 pages) detailing their motivations for pursuing a PhD in general and specifically the project described above, and contact information for 2 references that the selection committee can contact. Incomplete candidatures will not be processed.

 

Application deadline       Complete candidature files should be submitted to brian.katz@sorbonne-universite.fr and markus.noisternig@ircam.fr before 1-Oct-2018.

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6-6(2018-08-13) Post-docs at Idiap, Martigny, Switzerland

Dear Colleagues,

We currently have openings for two or three post-doctoral researchers in speech and
language processing at Idiap Research Institute:

 http://www.idiap.ch/education-and-jobs/job-10251

All the positions involve the theory and application of deep learning.  Whilst a
significant research element is envisaged, there are also applications involving
collaborations with local enterprises.

Idiap is located in French speaking Switzerland, although the lab hosts many
nationalities, and functions in English.  All positions offer quite generous salaries. 
More information is available on the institute's web site, http://www.idiap.ch/en

Several similar positions at PhD, post-doc and senior level are also available at the
institute in general.

 http://www.idiap.ch/en/join-us/job-opportunities

Sincerely,
--
Phil Garner
http://www.idiap.ch/~pgarner

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6-7(2018-08-17) 1 PhD position in affective computing at the Grenoble Alps University, France

1 PhD position in affective computing at the Grenoble Alps University
ATOS France and the Grenoble Informatics Laboratory (LIG) invite applications for a fully funded PhD position on 'Weakly-supervised learning of human affective behaviors from multimodal interactions with a chatbot'. The PhD will be co-supervised by Jean-Phillippe Vigne (ATOS) and Béatrice Bouchot (ATOS), Pr. Laurent Besacier (LIG) and Dr. Fabien Ringeval (LIG).


Thesis description
==================
The thesis targets three main objectives: 
1) the development of a weakly-supervised learning methodology for the semi-automatic annotation of affective information from speech and text produced by humans while interacting with a chatbot
2) the development of a module that performs a robust fusion of inputs? representations (speech + text) in order to infer attributes of affect in varying noisy conditions
3) an evaluation of the system?s robustness in different contexts of interaction with the chat-bot.

Recent advances in deep learning have shown promising results in many applications of affective computing [Picard-95], where ones of the most dominant tasks consist in quantifying attributes of human emotion, such as arousal, valence, or dominance [Russel-80], time-continuously from signals recorded by sensors [Wöllmer-08]. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) [Gers-99] have successfully been employed to model long-range contextual dependencies between attributes of affect and speech data [Eyben-10, Eyben-12, Ringeval-15], and convolutional neural networks (CNNs) have shown promising results for learning useful information from the raw signals when combined with LSTM-RNN in the so-called ?end-to-end? framework [Trigeorgis-16]. Recently, semi-supervised [Schmitt-16, Ghosh-16] and unsupervised [Cummins-18] methods of representation learning have shown the interest of exploiting resources from other domains in order to deal with the issue of data scarcity, which is of paramount importance for methods based on deep learning, as they need as many examples as possible to generalise well on expressions of affect produced ?in-the-wild? [Ringeval-18].

In this thesis, weakly-supervised methods based on deep learning will be exploited to perform semi-automatic annotation of human affective behaviour from speech and text ? either typed on a keyboard, or automatically retrieved from speech by an ASR. Context-aware novelty detection [Marki-15] based on deep LSTM auto-encoders will be used to detect novel affective content, and semi-supervised learning methods [Zhang-18] will be employed to enrich the model while following a curriculum learning [Lotfian-18]. Data exploited to build and evaluate the system will rely on the data collected during the project but also on existing publicly available datasets of emotion, including people with various culture, language, age, education, but also featuring different environments and contexts of interaction. Data automatically retrieved from social platforms like YouTube channels will be considered for automatically enriching the model in a ?virtuous circle? fashion.

The envisioned starting date is December 2018.


Requirements
============
We are looking for one candidate with a strong focus on deep learning for affective computing with the following profile:
+ Master?s degree with background in Machine Learning, Speech Processing, Affective Computing                 
+ Excellent programming skills (Python, Java, C/C++), knowledge of Keras/TensorFlow/Torch would be ideal
+ Ability to work independently and be self-motivated         
+ Excellent communication skills in English           
 

Applying
========
To apply, please email your application to: fabien.ringeval@imag.frlaurent.besacier@imag.frjean-philippe.vigne@atos.net and beatrice.bouchot@atos.net.
 
The application should consist of a single pdf file including:                  
+ a curriculum vitae showing academic records with tracks related to the themes of the thesis               
+ transcript of marks according to M1-M2 profile or last 3 years of engineering school      
+ statement letter expressing your interest in the position and your profile relevance             
+ contact and recommendation letter of at least one university referent                   
 
Incomplete applications will not be processed. Potential candidates will be invited for an interview with the supervisors.
 

Conditions of employment     
========================
You will be hired on a fixed-term contract (3 years contract ? CIFRE) at ATOS, a global leader in digital transformation.
 

Working at Grenoble (ATOS/LIG)       
==================================
You will be integrated in two teams with academic and industrial profiles: the GETALP team of the LIG, recognised for its research activities in the fields of speech and language processing, and the team Cognitive Intelligence from ATOS, who is specialised in Artificial Intelligence (AI) for the development of chatbots.

ATOS is a leader in digital services with pro forma annual revenue of circa ? 12 billion and circa 100,000 employees in 73 countries, serving a global client base. ATOS R&D team has a very active innovation spirit backed by a culture of Intellectual Property. Together these have led to numerous disruptive developments, including more than 1,500 patents. ATOS Grenoble (1000 collaborators) is focusing on AI, working with a variety of clients to implement solutions where they create value. ATOS leadership in Cloud technology, Cybersecurity and High-performance computing, along with our partnerships with major AI companies (e.g., Google), help us provide clients with the resources, expertise and support they need.

The LIG is one of the largest laboratories in Computer Science in France. It is structured as a Joint Research Center (Unité Mixte de Recherche) founded by the CNRS, the Grenoble Institute of Technology (Grenoble INP), the INRIA Grenoble Rhône-Alpes, and the Grenoble Alps University (UGA), which has recently been ranked as France?s number one university in eleven disciplines, including Computer Science & Engineering, in the latest Shanghai Academic Subject Rankings of World Universities 2017. The LIG hosts 17 research teams and three teams providing administrative and technical supports, which represent an overall of 450 collaborators including 205 permanent researchers, 143 PhD students, and 35 persons in supporting teams as identified in 2016.

The city of Grenoble is located on a plateau at the foot of the French Alps and is advertised to be the ?Capital of the Alps? due to its immediate proximity to the mountains. The IMAG building hosting the LIG is located on a landscaped campus of 175 hectares, which straddles Saint-Martin-d?Hères and Gières, and welcome around 40,000 students and researchers working in various research institutions. Thanks to this campus, UGA has been ranked as the eighth most beautiful universities in Europe by the Times Higher Education magazine in 2018. Overall, Grenoble as a city is the largest research center in France after Paris with 22,800 researchers.     
  

References
==========

[Cummins-18]         Nicholas Cummins, Shahin Amiriparian, Gerhard Hagerer, Anton Batliner, Stefan Steidl, and Björn Schuller, An image-based deep spectrum feature representation for the recognition of emotional speech, in Proceedings of ACM MM 2017, pp. 478?484, October 2017, ACM.
[Eyben-10]              Florian Eyben, Martin Wöllmer, Alex Graves, Björn Schuller, Ellen Douglas-Cowie and Roddy Cowie, On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues, Journal on Multimodal User Interfaces 3(1-2):7?19, March 2010, Springer Nature.
[Eyben-12]              Florian Eyben, Martin Wöllmer, and Björn Schuller, A multi-task approach to continuous five-dimensional affect sensing in natural speech, ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments 2(1):6, March 2012, ACM.
[Gers-99]                  Felix A. Gers, Jürgen Schmidhuber, and Fred Cummins, Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN 1999, pp. 850?855, ENNS.
[Ghosh-16]              Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency and Stefan Scherer, Representation learning for speech emotion recognition, in Proceedings of Interspeech 2016, pp. 3603?3607, September 2016, ISCA.
[Lotfian-18]             Reza Lotfian and Carlos Busso, Curriculum learning for speech emotion recognition from crowdsourced labels, arXiv:1805.10339, May 2018.
[Marki-15]                Erik Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency and Stefan Scherer, Representation learning for speech emotion recognition, in Proceedings of Interspeech 2016, pp. 3603?3607, September 2016, ISCA.
[Picard-95]               Rosalind W. Picard, Affective Computing, MIT Press.
[Ringeval-15]          Fabien Ringeval, Florian Eyben, Eleni Kroupi, Anil Yuce, Jean-Philippe Thiran, Touradj Ebrahimi, Denis Lalanne, and Björn Schuller, Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data, Pattern Recognition Letters, 66:25?30, November 2015, Elsevier.
[Ringeval-18]          Fabien Ringeval, Björn Schuller, Michel Valstar, Roddy Cowie, Heysem Kaya, Maximilian Schmitt, Shahin Amiriparian, Nicholas Cummins, Denis Lalanne, Adrien Michaud, Elvan Ciftçi, Hüseyin Güleç, Albert Ali Salah, and Maja Pantic, AVEC 2018 Workshop and challenge: Bipolar disorder and cross-cultural affect recognition, in Proceedings of AVEC?18, ACM MM, October 2018, ACM.
[Russel-80]               James A. Russel, A circumplex model of affect, Journal of personality and social psychology, 39(6):1161?1178, December 1980, APA.
[Schimtt-16]             Maximilian Schmitt, Fabien Ringeval, and Björn Schuller, At the border of acoustics and linguistics: Bag-of-Audio-Words for the recognition of emotions in speech. In Proceedings Interspeech 2016, pp. 495?499, San Fransisco (CA), USA, September 2016, ISCA.
[Trigeorgis-16]        George Trigeorgis, Fabien Ringeval, Raymond Brueckner, Erik Marchi, Mihalis Nicolaou, Björn Schuller and Stefanos Zafeiriou, Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network, in Proceedings ICASSP 2016, pp. 5200?5204, Shanghai, China, April 2016, IEEE.
[Wöllmer-08]           Martin Wöllmer, Florian Eyben, Stephan Reiter, Björn Schuller, Cate Cox, Ellen Douglas-Cowie, and Roddy Cowie, Abandoning emotion classes-towards continuous emotion recognition with modelling of long-range dependencies, in Proceedings of Interspeech 2008, Brisbane, Australia, pp. 597?600, ISCA.
[Zhang-18]              Zixing Zhang, Jin Han, Jun Deng, Xinzhou Xu, Fabien Ringeval, and Björn Schuller, Leveraging unlabelled data for emotion recognition with enhanced collaborative semi-supervised learning, IEEE Access, 6, April 2018, IEEE.

 
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6-8(2018-08-20) PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.

Subject: PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.

Job description:

 

The Medical University of Vienna (MUV), Austria, seeks to fill a position of a PhD-student within the project ?Objective differentiation of dysphonic voice quality types?. The candidate must hold a master?s degree, preferably in (one of) the fields of sound engineering, acoustical engineering, audio signal processing, or similar. The work will be conducted at the Division of Phoniatrics-Logopedics within the Department of Otorhinolaryngology of the MUV.

The workgroup hosting the project is interested in the assessment of voice parameters relevant to the medical diagnosis and clinical care of voice disorders. A focus is given to functional assessment of voice, especially to the objective description of voice quality. The levels of description include kinematics of voice production, voice acoustics, and auditory perception of voice. Clinical studies are conducted with a laryngeal high-speed camera that records vocal fold vibration at 4000 frames per second. Microphone signals of the voice are recorded in parallel. Vibratory patterns of the vocal folds are analysed visually and computationally via modelling. Trajectories of vocal fold edges, spatial arrangements thereof, and glottal area waveforms are analysed. Regarding acoustics, analysis of audio recordings involves the implementation, testing, and training of specialized synthesizers for pathological voices. On the level of auditory perception, listening experiments are conducted, especially experiments involving discrimination tasks.

Mandatory skills of the candidate are MATLAB programming, speech signal processing, psychoacoustics, good knowledge of English, good communication skills, and excellent analytical thinking. Optional skills of the candidate are knowledge of German, experience in a health care profession, image and video processing, Python, PureData, object-oriented programming, software engineering, version control (Subversion, Git, or similar), SQL, and XML.

The project duration is 4-5 years. The Austrian Science Fund (FWF) budgets for doctoral candidates a gross salary of 2.112,40 Euro per month. Application documents can be submitted to philipp.aichinger@meduniwien.ac.at by October 31st, 2018. Interviews are planned for November 2018.The project is planned to start in December 2018.

Information regarding the beautiful city of Vienna can be found at https://www.meduniwien.ac.at/web/en/international-affairs/living-in-vienna/.

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6-9(2018-08-27) POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique, Laboratoire national de métrologie et d'essais, Trappes, France

POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique

 

Localisation : Laboratoire national de métrologie et d'essais, Trappes (78)

 

REF : ML/VOX/DE

 

CONTEXTE :

 

Le projet ANR VoxCrim (2017-2021) propose d’objectiver scientifiquement les possibilités de mise en œuvre d’une comparaison de voix dans le domaine criminalistique. Deux objectifs principaux : a) mettre en place une méthodologie permettant d’assurer l’efficacité et la compétence des laboratoires réalisant des comparaisons de voix, b) établir des standards de mesures objectives.

Il est nécessaire que les outils et méthodologies utilisés dans la comparaison de voix soient évalués, et que leur utilisation soit effectuée dans un cadre explicable et transparent. Les actions menées dans le projet permettront ainsi de faciliter le traitement d’une comparaison de voix dans les services de police et permettront de renforcer la recevabilité de la preuve auprès des tribunaux.

Le laboratoire national de métrologie et d’essais (LNE) apporte au projet son expertise en métrologie, normalisation, accréditation et comparaison inter-laboratoire, dans le but de constituer une solution méthodologique pratique permettant de rendre le processus de comparaison de voix transparent et explicable.

 

MISSIONS :

 

Les missions confiées s’organisent en trois tâches :

-              Spécifications du protocole de validation des méthodes de comparaison de voix, plus spécifiquement dans le domaine de la criminalistique. En s’appuyant sur l’existant en termes de normes et méthodologies de référence, le (la) post-doctorant(e) identifiera les besoins et possibilités pour la mise en place d’un protocole de référence.

-              Le (la) post-doctorant(e) vérifiera l’adéquation du protocole identifié avec les métriques de comparaison de voix identifiées par les chercheurs des laboratoires d’informatique et de phonétique associés au projet. Il (elle) s’assurera également de la compatibilité du protocole avec les méthodes de travail des centres scientifiques de la Police et de la Gendarmerie, membres du projet.

-              Il (elle) collaborera à l’organisation d’une comparaison inter-laboratoire s’appuyant sur ce protocole.

 

Le (la) post-doctorant(e) bénéficiera du soutien de différentes équipes du LNE dans la menée de ses travaux (équipes évaluation des systèmes de traitement de l’information,  mathématiques-statistiques, et métrologie), et sera en interaction régulière avec les autres laboratoires et centres scientifiques membres du projet.

Des publications (et présentations, le cas échéant) en conférences et journaux internationaux sont attendues du (de la) post-doctorant(e).

 

Bibliographie : Bonastre, J. F., Kahn, J., Rossato, S., & Ajili, M. (2015). Forensic speaker recognition: Mirages and reality. In Speech Production and Perception: Speaker-Specific Behavior. hal-01473992.

 

DUREE :

 

18 mois. Début en janvier 2019.

 

PROFIL :

 

Vous êtes titulaire d’un doctorat en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la parole.

Vous possédez des connaissances en méthodologie d’évaluation et en biométrique vocale.

 

Pour candidater, merci d’envoyer votre CV et lettre de motivation à l’adresse recrut@lne.fr en rappelant la référence : ML/VOX/DE

 

====================================================

Agnes Delaborde, PhD

Ingénieur de recherche en évaluation IA & robotique (Research engineer in AI and robotics evaluation)

Direction des essais – DE536

agnes.delaborde@lne.fr

Tél. : +33 (0)1 30 69 11 50 - Mob. : +33 (0)6 26 72 69 80

 

 

Laboratoire national de métrologie et d'essais

29 avenue Roger Hennequin 78197 Trappes Cedex - lne.fr

 

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6-10(2018-08-31) Post Doctoral Position (12 months), Natural Language Processing, INRIA-Loria, Nancy, France

Post Doctoral Position (12 months), Natural Language Processing: ?Online hate speech against migrants?

Keywords: hate speech, migrants, social media, natural language processing.

Supervisors : Irina Illina and Dominique Fohr. The applicant will also collaborate with CREM Laboratory

Start: end of 2018 ? begin of 2019

Location: INRIA-Loria, Nancy, France

Duration: 1 year

To apply:  send the following documents to illina@loria.fr and  dominique.fohr@loria.fr  as soon as possible and no later than September 25th, 2018:

  • CV

  • motivation letter

  • PhD thesis if already completed, or a description of the work in progress otherwise

  • a copy of your publications

    • a recommendation letter from the supervisor of your PhD thesis, and up to two other recommendation letters.

    The ideal applicant should have:

    • A PhD in NLP

    • A solid background in statistical machine learning.

    • Strong publications.

    • Solid programming skills to conduct experiments.

    • Good level in English.

       

       

      Context:

      According to the 2017 International Migration Report, the number of international migrants worldwide has continued to grow rapidly in recent years, reaching 258 million in 2017, up from 220 million in 2010 and 173 million in 2000. In 2017, 64 per cent of all international migrants worldwide ? equal to 165 million international migrants ? lived in high-income countries; 78 million of them were residing in Europe. A key reason for the difficulty of EU leaders to take a decisive and coherent approach to the refugee crisis has been the high levels of public anxiety about immigration and asylum across Europe. Indeed, across the EU, attitudes towards asylum and immigration have hardened in recent years because of: (i) the increase in the number and visibility of migrants in recent years, (ii) the economic crisis and austerity policies enacted since the 2008 Global Financial Crisis, (iii) the role of the mass media in influencing public and elite political attitudes towards asylum and migration. Refugees and migrants tend to be framed negatively as a problem, potentially nourishing.

      The BRICkS ? Building Respect on the Internet by Combating Hate Speech ?  EU project has revealed a significant increase of the use of hate speech towards immigrants and minorities, which are often blamed to be the cause of current economic and social problems. The participatory web and the social media seem to accelerate this tendency, accentuated by the online rapid spread of fake news which often corroborate online violence towards migrants.

      More and more audio/video/text appear on Internet each day. About 300 hours of multimedia are uploaded per minute. In these multimedia sources, manual content retrieval is difficult or impossible. The classical approach for spoken content retrieval from multimedia documents is an automatic text retrieval. Automatic text classification is one of the widely used technologies for the above purposes. In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. We will use these methodologies to perform one of the important tasks of text classification: automatic hate speech detection.

      Our methodology in the hate speech classification will be related on the recent approaches for text classification with neural networks and word embeddings. In this context, fully connected feed forward networks (Iyyer et al., 2015; Nam et al., 2014), Convolutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015) and also Recurrent/Recursive Neural Networks (RNN) (Dong et al., 2014) have been applied.

    •  

       

       

      Objectives:

      Within this context and problematic, the post-doc position aims to analyze hate speech towards migrants in social media and more particularly on Twitter. This post-doc position aims at proposing concepts and software components (Hate Speech Domain Specific Analysis and related software tools in connection with migrants in social media) to bridge the gap between conceptual requirements and multi-source information from social media. Automatic hate speech detection software will be experimented in the modeling of various hate speech phenomenon and assess their domain relevance. 

      The language of the analysed messages will be primarily French, although links with other languages (including messages written in English) may appear throughout the analysis.

      • References

        Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc

        Delgado R., Stefancic J. (2014), ?Hate speech in cyberspace?, Wake Forest Law Review, 49.

        Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54.

        Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112.

        Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691.

        Kim, Y. (2014). ?Convolutional neural networks for sentence classification?. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751.

        King R. D., Sutton G. M. (2013). High times for hate crimes: Explaining the temporal clustering of hate-motivated offending. Criminology, 51 (4), 871?894.

        Mikolov, T., Yih, W.-t., and Zweig, G. (2013a). ?Linguistic regularities in continuous space word representations?. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746-751.

        Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc.

        Nam, J., Kim, J., Loza Menc__a, E., Gurevych, I., and Furnkranz, J. (2014). ?Large-scale multi-label text classification ? revisiting neural networks?. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, volume 8725, pages 437-452.

        Schieb C, Preuss M (2016), Governing Hate Speech by Means of Counter Speech on Facebook, 66th ICA Annual Conference, Fukuoka, Japan.

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6-11(2018-09-02) CDD IRISA, Rennes, France

L'équipe Expression de l'IRISA ouvre un CDD de 24 mois sur le déploiement mobile d'un système de synthèse de la parole.

 

Mots-clés : synthèse de la parole, intelligence artificielle, machine learning, agents conversationnels.

 

Détails :

- offre d'emploi : pièce jointe et ici : https://www-expression.irisa.fr/files/2018/09/fiche_de_poste.pdf

- Équipe Expression : http://www-expression.irisa.fr/fr/

- IRISA : http://www.irisa.fr/

- Bac + 5 ou Bac + 2 avec compétence Android / iOS

- Date limite de candidature : 5 octobre 2018

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6-12(2018-09-03) PhD position and Post-doc position: Privacy-respecting dialog systems,Saarland University,Germany

PhD position and Post-doc position: Privacy-respecting dialog systems
=============================================
(Computational Linguistics, Computer Science or similar)

Conversational interfaces based on deep learning are becoming more and
more ubiquitous. However, the massive amounts of stored speech and
text data that is needed for training state-of-the-art models raises
serious privacy concerns for its users. Each spoken message may
potentially reveal information about the user's personality, may
contain critical information (credit card numbers, passwords, etc.),
and may convey sensitive information (ethnicity, age, health status,
etc.). Voice recordings may even be malevolently used to build
synthesized voiced to impersonate users.

The Spoken Language Systems group at Saarland University is seeking
new ways to provide dialog technology that is 'private by design' by
means such as e.g. privacy-preserving machine learning. To this end,
we are anticipating the availability of a PhD position and a Post-Doc
position starting at the beginning of 2019.

Ideal candidates for either position would have:

  1. A good understanding of not just NLP, but of dialog phenomena in
     particular. Here, an understanding of how privacy-relevant
     information may arise as a result of dialog behavior (rather than
     as part of a single utterance) is desirable.

  2. Excellent knowledge of machine learning, experience with
     weakly-supervised methods a plus.

  3. Knowledge of and experience with scientific evaluation
     methodologies.

  4. Excellent programming skills, experience with RESTful APIs a plus.

  5. Experience with architecting large, heterogeneous, modular and
     distributed systems.

Salaries: The PhD position will be 75% of full time on the German E13
scale (TV-L). The Post-Doc position will be 100% of the full time on
the German E13 scale (TV-L). The appointments will be for three years
with possible extensions subject to follow-up funding.

About the department: The department of Language Science and
Technology is one of the leading departments in the speech and
language area in Europe. The flagship project at the moment is the CRC
on Information Density and Linguistic Encoding. Furthermore, the
department is involved in the cluster of excellence Multimodal
Computing and Interaction. It also runs a significant number of
European and nationally funded projects. In total it has seven faculty
and around 50 postdoctoral researchers and PhD students.

How to apply:

Please send us:
* a letter of motivation,
* your CV,
* your transcripts,
* a list of publications,
* and the names and contact information of at least two references,

as a single PDF or a link to a PDF if the file size is more than 3 MB.

Please apply by October 10th, 2018.

Contact: Applications and any further inquiries regarding the project
should be directed to:

* Thomas.Kleinbauer@lsv.uni-saarland.de
* Dietrich.Klakow@lsv.uni-saarland.de

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6-13(2018-09-08) Enseignant vacataire Université de Franche-Comté, Besançon, France
Le département Sciences du Langage & FLE de l'Université de Franche-Comté (site de Besançon, SLHS) cherche un.e enseignant.e vacataire pour le TD de « Phonétique et multimodalité de la parole », en 2è année de licence Sciences du langage, au semestre 1, 12h par TD (2 voire 3), sur le créneau du vendredi 8-9h, 10-11h et/ou 13-14h.

Informations :
? Description : Ce cours s?intéresse à l?émergence de la parole des points de vue ontogénétique et phylogénétique. Il explore les modifications anatomiques et physiologiques qui rendent la parole possible. Le cours s?intéresse aussi aux différentes modalités (vocale, verbale, gestuelle) que les humains utilisent pour communiquer au quotidien.
? Objectifs pédagogique : Connaître l?importance de la parole dans la communication humaine, et les autres modalités qui la complètent
- Se familiariser avec les questions que posent les sciences humaines à propos de l?origine du langage.
- S?initier d?un point de vue phonétique à l?unité et à la diversité des langues
- Se familiariser avec les mécanismes de la phonation et approfondir les connaissances articulatoires du système phonétique français
- Renforcer les compétences en transcription phonétique et s?initier à l?analyse acoustique de la parole.
 
Le/la candidat.e devra :
 - soit être étudiant.e âgé.e de moins de vingt-huit ans au 1er septembre de l'année universitaire et inscrit.e.s en vue de la préparation d'un diplôme du troisième cycle, ou :
- justifier d'une activité professionnelle principale d'une durée de 900 heures sur une période de 12 mois (décret du 16 septembre 2004).
 
Contact : Sophie Mariani-Rousset (smariani@univ-fcomte.fr)
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6-14(2018-09-13) Senior Research and Development Engineer (m/f), ELDA, Paris

The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context, acting as the distribution agency of the European Language Resources Association (ELRA), is currently seeking to fill an immediate vacancy for a Senior Research and Development Engineer position.

Senior Research and Development Engineer (m/f)

Under the supervision of the CEO, the responsibilities of the Senior R&D Engineer include designing, developing, documenting, deploying and maintaining tools, software components or web applications for language resource production and management, as well as carrying out quality control and assessment of language resources.
He/she will be in charge of managing the current language resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded research and development projects.

This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.

Profile:
?    PhD in Computer Science, Electrical Engineering, Natural Language Processing, or equivalent
?    Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.)
?    Experience in managing a multi-disciplinary team
?    Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)
?    Proficiency in Python
?    Hands-on experience in Django
?    Good knowledge of Javascript and CSS
?    Knowledge of a distributed version control system (Git, Mercurial)
?    Knowledge of SQL and of RDBMS (PostgreSQL preferred)
?    Knowledge of XML and of standard APIs (DOM, SAX)
?    Good knowledge of basic Computer Science algorithms
?    Familiarity with open source and free software
?    Knowledge of a statically typed functional programming language (OCaml preferred) is a strong plus
?    Proficiency in French and English, with strong writing and documentation skills in both languages
?    Dynamic and communicative, flexible to work on different tasks in parallel
?    Ability to work independently and as part of a multidisciplinary team
?    Citizenship (or residency papers) of a European Union country

Salary: Commensurate with qualifications and experience.

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to:

ELDA
9, rue des Cordelières
75013 Paris
FRANCE
Fax : 01 43 13 33 30
Mail : job@elda.org

ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA/ELRA, visit:
http://www.elda.org

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6-15(2018-09-22) Doctoral student (Speech Technology, Cognitive Science), Tampere University of Technology, Finland

Doctoral student (Speech Technology, Cognitive Science),  Tampere University of Technology, Finland


We are inviting applications for the position of Doctoral Student in the areas of speech technology and cognitive science at Tampere University of Technology (TUT), Laboratory of Signal Processing. The successful candidate will become a member of a newly formed research group named Speech and Cognition, led by Assistant Professor Okko Räsänen. In addition to research work, the candidate will commit to the pursuit of a doctoral degree in science (technology) at TUT. The job will consist of the following duties: • Research work on a mutually agreed doctoral research topic • Completion of mandatory studies for a D.Sc. (tech.) degree • Participation to the Doctoral Program of Computing and Electrical Engineering at TUT • Assisting tasks in teaching and in other activities of the research group
 
The broad scope of the position is related to the study of language acquisition and processing by humans and artificial computational systems. Potential topics include: 1. development of computational models of unsupervised and multimodal language learning and speech perception 2. development of algorithms and tools for analyzing acoustic and linguistic patterns in large-scale naturalistic audio recordings.
 
More precise goals of the thesis project will be planned together with the candidate. The work in the position will be closely integrated to several ongoing Academy of Finland research projects and their international collaboration networks.  The current contract will be made for a fixed term period until 31.8.2021 with a view for extension (with an initial probationary period of 6 months). Target completion time for a doctoral degree is 4 years. The commencement date will be as soon as possible, as mutually agreed.  The salary will be based on both the job demands and the employee's personal performance level in accordance with the University Salary System. According to the criteria applied to teaching and research staff, the position of a Doctoral Student is placed on job demands levels 2–4. A typical starting salary for a Doctoral Student at the beginning of their studies is 2330–2450 eur.  Exceptional Master’s students of TUT, who are close to graduation, can be also considered for the position. In this case, the candidate is first employed as a Research Assistant to carry out a master’s thesis project (6 months) on the topic and, upon a successful thesis project, with the possibility to continue to doctoral studies. Salary during master’s thesis project will correspond to job demands level 1. Requirements: The successful candidate must hold a master’s degree or to be close to graduation in a discipline related to the job, for example Computer Science, Signal Processing, Mathematics, Artificial Intelligence or Machine Learning. Candidates from Linguistics, Psychology, Neurosciences, or other field related to language or developmental research will also be considered, given that the candidate has a sufficiently strong technical background.  Basic programming skills and experience with MATLAB, Python, or comparable programming languages are required. Good written and spoken English skills, capability for
team work, and open mindset towards cross-disciplinary research are also essential. Skills in statistical analysis and previous research experience are counted as an advantage.  The successful candidate must either already be a PhD student at Tampere University of Technology or apply for post-graduate studies at the university. More information on the admission process and requirements: http://www.tut.fi/en/admissions/doctoral-studies-p... For more information, please contact: Assistant Professor Okko Räsänen, email: okko.rasanen@tut.fi How to apply: Applications must be submitted by TUT online application form at https://tut.rekrytointi.com/paikat/?o=A_A&jid=28 .  
 Closing date for applications is 30 September 2018 (24.00 EEST / 21.00 UTC). The most promising candidates will be interviewed in person or in a teleconference. The interviews will take place during the last week of application period and the first week of October, and therefore it is advisable to submit an application as soon as possible.  The following documents should be attached to the application in .pdf format:  • motivation letter  • CV, including contact details of possible referees • copy of Master’s degree diploma (if applicable) and a transcript of completed studies with course grades • copies of certificates related to the applicant's language proficiency
 About the Speech and Cognition Group: The research of Speech and Cognition group covers speech communication from both language technology and cognitive science points of view. Central research questions are related to how humans learn to understand and produce speech in interaction with their environment, how speech perception and production operate and how they are related to other cognitive capabilities such as memory and learning, and how similar language and cognitive skills could be implemented in man-made computational systems. The primary research method of the group is computational modeling of these phenomena, combining machine learning and signal processing to spoken language and other sensory data available to language learning children. The group also works on various technological applications related to, e.g., spoken language health technology and automatic analysis of large-scale speech recordings. Research of the group is conducted in collaboration with researchers across areas such as speech technology, linguistics, psychology, acoustics, neuroscience, and clinical medicine. About the research environment: Finland is among the most stable, free and safe countries in the world, based on prominent ratings by various agencies. It is also ranked as one of the top countries as far as social progress is concerned. Tampere is counted among the major academic hubs in the Nordic countries and offers a dynamic living environment. Tampere region is one of the most rapidly growing urban areas in Finland and home to a vibrant knowledge-intensive entrepreneurial community. The city is an industrial powerhouse that enjoys a rich cultural scene and a reputation as a centre of Finland’s information society. Read more about Finland and Tampere: • https://www.visitfinland.com/about-finland/ • https://finland.fi/ • https://tem.fi/documents/1410877/2888440/SIS_MIN_E... • https://visittampere.fi/en/

 The new Tampere University and higher education community begin their operations on 1 January 2019. Tampere University of Technology, the University of Tampere and Tampere University of Applied Sciences are building a unique environment for multidisciplinary, inspirational and high-impact research and education and a hub of expertise in technology, health and society. Read more: https://www.tampere3.fi/en
 

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6-16(2018-09-17) Associate Linguist [Français]

 

 

Intitulé du poste :

Associate Linguist [French]

Champs linguistiques  :

Phonétique, Phonologie, Morphologie, Sémantique, Syntaxe, Lexicographie, TAL

Lieu :

Paris, France

Description du poste :

En tant qu’Associate Linguist, vous annoterez et réviserez des données linguistiques en français.  L’Associate Linguist contribuera également à un certain nombre de tâches en traitement automatique des langues, dont :

  • Transcription phonétique/phonémique d’entrées lexicales
  • Analyse de données acoustiques pour évaluer la synthèse vocale
  • Annotation et révision de données linguistiques
  • Labellisation de textes, désambiguisation, expansion, and normalisation des données
  • Annotation d’entrées lexicales en respectant les codes de référence
  • Evaluation des outputs système
  • Dérivation de données en TAL
  • Capacité à travailler de manière indépendante avec précision

Compétences requises:

  • Locuteur de langue maternelle française, parfaite maîtrise de l’anglais
  • Connaissance en transcriptions phonétiques et phonologiques
  • Familiarité avec les techniques et outils de synthèse de la parole et de reconnaissance vocale
  • Expérience en annotation
  • Connaissances en phonétique, phonologie, sémantique, syntaxe, morphologie et lexicographie
  • Excellentes compétences en communication orale et écrite
  • Attention aux détails et compétences organisationnelles 

 

Compétences désirées :

  • Diplôme en linguistique théorique et computationnelle et TAL
  • Capacité à saisir rapidement les concepts techniques et les outils conçus en interne
  • Vif intérêt pour la technologie et compétences en informatique
  • Compétences en écoute de données orales
  • Compétences en saisie de clavier rapide et précise
  • Familiarité avec les logiciels de transcription
  • Compétences en édition, correction grammaticale et orthographique
  • Compétences en recherche

 

CV + lettre de motivation en Anglais : maroussia.houimli@adeccooutsourcing.fr

2730E brut/mensuel + 50% Pass Navigo + Mutuelle

 

 

Maroussia HOUIMLI

Responsable recrutement

Accueil en entreprise & Evénementiel et Marketing-Vente

  

T 06.24.61.08.43

E maroussia.houimli@adeccooutsourcing.fr

 

   

21 Boulevard Voltaire, 75011 - Paris

www.adecco.fr/outsourcing/

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6-17(2018-09-18) 3-year Postdoctoral Researcher in Multilingual Speech Processing, IRISA, Rennes, France (updated)

3-year Postdoctoral Researcher in Multilingual Speech Processing


CONTEXT

The Expression research team focuses on expressiveness in human-centered data. In this context, the team has a  strong activity in the field of speech processing, especially text-to-speech (TTS). This activity is denoted by  regular publications in top international conferences and journals, exposing contributions in topics like machine learning (including deep learning), natural language processing, and speech processing. Team Expression takes part in multiple collaborative projects.Among those,the current position will take part in a large European H2020 project focusing on the social integration of migrants in Europe.

Team’s website: https://www-expression.irisa.fr/

PROFILE Main tasks:

1. Design multilingual TTS models (acoustic models, grapheme-to-phoneme, prosody, text         normalization…)

2. Take part in porting the team’s TTS system for embedded environments

3. Develop spoken language skill assessment methods

Secondary tasks: 1. Collect speech data

2. Define use cases with the project partners

Environment: The successful candidate will integrate a team of other researchers and engineers working on the same topics.

Required qualification: PhD in computer science or signal processing

Skills: ● Statistical machine learning and deep learning

● Speech processing and/or natural language processing

µ● Strong object-oriented programming skills

µ● Android and/or iOS programming are a strong plus

CONTRACT Duration: 36 months, full time

Salary: competitive, depending on the experience.

Starting date: latest mid June 2019

APPLICATION & CONTACTS Send a cover letter, a resume, and references by email to:

● Arnaud Delhay,arnaud.delhay@irisa.fr ;

● Gwénolé Lecorvé, gwenole.lecorve@irisa.fr ;

● Damien Lolive, damien.lolive@irisa.fr. Application will be processed on a daily basis.

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6-18(2018-09-20) Inria is seeking a Technical Project Manager for a new European (H2020 ICT) collaborative project COMPRISE, INRIA, Nancy, France

Inria is seeking a Technical Project Manager for a new European (H2020 ICT) collaborative
project called COMPRISE.

COMPRISE is a 3-year Research and Innovation Action (RIA) aiming at new cost-effective,
multilingual, privacy-driven voice interaction technology. This will be achieved through
research advances in privacy-driven machine/deep learning, personalized training,
automatic data labeling, and tighter integration of speech and dialog processing with
machine translation. The technology will be based on existing software toolkits (Kaldi
speech-to-text, Platon dialog processing, Tilde text-to-speech), as well as new software
resulting from these research efforts. The consortium includes academic and industrial
partners in France (Inria, Netfective Technology), Germany (Ascora, Saarland University),
Latvia (Tilde), and Spain (Rooter).

The successful candidate will be part of the Multispeech team at Inria Nancy (France). As
the Technical Project Manager of H2020 COMPRISE, he/she will be responsible for animating
the consortium in daily collaboration with the project lead. This includes orchestrating
scientific and technical collaborations as well as reporting, disseminating, and
communicating the results. He/she will also lead Inria?s software development and
demonstration tasks.

Besides the management of COMPRISE, the successful candidate will devote half of his/her
time to other activities relevant to Inria. Depending on his/her expertise and wishes,
these may include: management of R&D projects in other fields of computer science,
involvement in software and technology development and demonstration tasks, building of
industry relationships, participation in the setup of academic-industry collaborations,
support with drafting and proofreading new project proposals, etc.

Ideal profile:
- MSc or PhD in speech and language processing, machine learning, or a related field
- at least 5 years' experience after MSc/PhD, ideally in the private sector
- excellent software engineering, project management, and communication skills

Application deadline: October 12, 2018

Starting date: December 1, 2018 or January 1, 2019
Duration: 3 years (renewable)
Location: Nancy, France
Salary: from 2,300 to 3,700 EUR net/month, according to experience

For more details and to apply:
https://jobs.inria.fr/public/classic/en/offres/2018-01033

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6-19(2018-09-20) PhD grant at Universidad Politécnica de Madrid, Spain

PhD grant at Universidad Politécnica de Madrid


BIOMARKERS FOR THE DIAGNOSIS AND EVALUATION OF PARKINSON'S DISEASE BASED ON SPEECH AND OCULOGRAPHIC MULTIMODAL STUDIES
Laboratories:  Bioengineering and Optoelectronics Research Group
http://www.byo.ics.upm.es 
Doctoral school:  ETSI Telecomunicación, Universidad Politécnica de Madrid 
Discipline:   Neuroscience, machine learning, digital signal processing 
Supervision:  Juan Ignacio Godino Llorente 
Keywords:  Parkinson’s disease, speech, oculographic signals, multimodal evaluation, early detection 
Research context:   
This thesis project is placed in the context of the BIOMARKERS FOR THE DIAGNOSIS AND EVALUATION OF PARKINSON'S DISEASE BASED ON SPEECH AND OCULOGRAPHIC MULTIMODAL STUDIES (DPI2017-83405-R), financed by the Spanish Ministry of Economy and Competitiveness. 
Summary of the project:
Parkinson's disease is a chronic degenerative disorder affecting the dopamine production centers in the basal ganglia and which is mainly manifested with dysfunctions in motor systems. The disease affects 2% of the population over 60 years but its prevalence is likely to increase due to the aging trend of the world population. In addition to affecting the quality of life of patients and their environment, the disease carries a loss of productivity and high costs for health systems, so early diagnosis and treatment are vital to alleviate these negative effects. However, to date, there are not early and noninvasive markers of the disease. The literature has identified that voice and oculographic signals are affected even in pre-symptomatic stages, but this has not been exploited to design robust diagnosis and screening systems. Therefore this project aims at employing voice and oculographic signals as biomarkers for the design of automatic detection and screening systems based on digital signal processing techniques. To do this a phonetic-articulatory analysis of speech together with an analysis of eye movements (saccades, fixations, smooth pursuit...) analysis will be performed. The project objectives are relevant to the challenge 'health, demographic change and wellbeing' aiming at alleviating the cost associated with the disease on the European healthcare system. 
Candidate profile:  
We are looking for dynamic, creative, and motivated candidates with scientific curiosity, strong problem solving skills, the ability to work both independently and in a team environment, and the desire to push their knowledge limits and areas of confidence to new domains. The candidate should have a Master in Bioengineering, Computer Science, Acoustics, Electronic Engineering, Multimodal Interfaces, or Signal Processing, and experience in signal processing, machine learning, and information retrieval from complex data. A strong interest in bioengineering and multi-disciplinary applications is necessary.   It is not expected that the candidate will have already all the skills necessary, but a willingness and ability to rapidly step into new domains. 
Summary of conditions:
 Full time work (37,5h/week)  Contract duration: 4 years.  Life Insurance.  Estimated Incorporation date: Beginning of 2019.   Specific conditions of the call  
Application: 
Interested candidates should send a CV, transcript of Master’s degree courses, a cover letter (limit 2 pages) detailing their motivations for pursuing a PhD in general and specifically the project described above, and contact information for 2 references that the selection committee can contact. 
Application deadline:       
Complete candidature files should be submitted to ignacio.godino@upm.es before October 10th, 2018.
See also http://www.byo.ics.upm.es/BYO/noticias/phd-position

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6-20(2018-08-20) PhD student opportunity at LTCI, Telecom ParisTech, Paris, France

A PhD student opportunity is now available at LTCI, Telecom ParisTech,
Paris, France (https://www.telecom-paristech.fr/eng,
https://ltci.telecom-
paristech.fr/about-the-lab/?lang=en)

Framework:
**********
Groups are a fascinating interdisciplinary phenomenon. They can be
defined as bounded and structured entities that emerge from the
purposive, interdependent actions of individuals. One of the current
open challenges on automated groups? analysis is to provide
computational models of higher level concepts called emergent states,
that is states emerging as results of affective, behavioral and
cognitive interactions among the members of a group. Cohesion is one
of these states. It is a dynamic process that is reflected in the
tendency for a group to stick together and remain united in the pursuit of
its instrumental objectives and/or for the satisfaction of members?

affective needs. Cohesion is considered as a highly valued group
property serving crucial roles for group effectiveness and
performance. Scholars proposed theoretical models of cohesion having

from one to five dimensions.

Among these dimensions, the task and social ones were the most
investigated. The task dimension concerns the extent to which group
members are united to achieve the group?s goals and objectives; the
social dimension refers to the social relationships within the group
(e.g. the extent to which group members like each other). The thesis
will focus on the development of a computational model of cohesion
among humans, able to integrate its task and social dimensions and
also accounting for their relationship and their development over time.
This work will be conducted in the framework of the ANR JCJC French
national project GRACE (Groups? Analysis for automated Cohesion

Estimation).


Tasks:
******
- State-of-the-art on cohesion to identify which are its most suitable
and frequent multimodal behavioral descriptors. State-of-the-art will
span several research fields, including sociology, psychology, and
computer science
- Computation of multimodal behavioral descriptors of cohesion
- Designing and performing experiments to collect a multimodal data
set on cohesion
- Designing, implementing, and evaluating a computational model of
cohesion

Profile:
********
The ideal candidate should have a strong academic background in one or
more of the following fields: Computer Science, AI, Machine learning,
Human- Computer Interaction, Information Technology, Affective
Computing, Social Signal Processing, or closely related fields. In
addition to a passion for science and programming, you should be open
to extend your thinking to the issues linked to Human-Computer
Interaction. Moreover, the ideal candidate should
have:
- Interest in multidisciplinary research at the interface between
computer science and sociology/psychology
- Excellent academic track record
- Good command of English (written and spoken)
- Strong programming skills (C++/Python)
- Very good communication skills, commitment, independent working
style as well as initiative and team spirit

Offer:
******
Starting date: between winter 2018 and spring 2019. This is flexible
and can be negotiated with the supervisor within the above-mentioned

time frame.

Application deadline: the evaluation of the PhD candidates starts
immediately and it will continue until the position is filled.
To apply please send by email to giovanna.varni@telecom-paristech.fr a
single pdf file including:
- A cover letter stating your research interests and how they could be
related to the research topic the thesis focuses on.
- A detailed CV
- Transcripts of records of your MSc
- List of at least 2 referees
- Recommendation letters

You are encouraged to contact Prof. Giovanna Varni for more information.
Please quote ?PhD position? in the email subject for both asking
information and application.
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6-21(2018-10-05) POST DOC 18 mois, Laboratoire national de métrologie et d'essais, Trappes (78), France

 

POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique

 

Localisation : Laboratoire national de métrologie et d'essais, Trappes (78)

 

REF : ML/VOX/DE

 

CONTEXTE :

 

Le projet ANR VoxCrim (2017-2021) propose d’objectiver scientifiquement les possibilités de mise en œuvre d’une comparaison de voix dans le domaine criminalistique. Deux objectifs principaux : a) mettre en place une méthodologie permettant d’assurer l’efficacité et la compétence des laboratoires réalisant des comparaisons de voix, b) établir des standards de mesures objectives.

Il est nécessaire que les outils et méthodologies utilisés dans la comparaison de voix soient évalués, et que leur utilisation soit effectuée dans un cadre explicable et transparent. Les actions menées dans le projet permettront ainsi de faciliter le traitement d’une comparaison de voix dans les services de police et permettront de renforcer la recevabilité de la preuve auprès des tribunaux.

Le laboratoire national de métrologie et d’essais (LNE) apporte au projet son expertise en métrologie, normalisation, accréditation et comparaison inter-laboratoire, dans le but de constituer une solution méthodologique pratique permettant de rendre le processus de comparaison de voix transparent et explicable.

 

MISSIONS :

 

Les missions confiées s’organisent en trois tâches :

-              Spécifications du protocole de validation des méthodes de comparaison de voix, plus spécifiquement dans le domaine de la criminalistique. En s’appuyant sur l’existant en termes de normes et méthodologies de référence, le (la) post-doctorant(e) identifiera les besoins et possibilités pour la mise en place d’un protocole de référence.

-              Le (la) post-doctorant(e) vérifiera l’adéquation du protocole identifié avec les métriques de comparaison de voix identifiées par les chercheurs des laboratoires d’informatique et de phonétique associés au projet. Il (elle) s’assurera également de la compatibilité du protocole avec les méthodes de travail des centres scientifiques de la Police et de la Gendarmerie, membres du projet.

-              Il (elle) collaborera à l’organisation d’une comparaison inter-laboratoire s’appuyant sur ce protocole.

 

Le (la) post-doctorant(e) bénéficiera du soutien de différentes équipes du LNE dans la menée de ses travaux (équipes évaluation des systèmes de traitement de l’information,  mathématiques-statistiques, et métrologie), et sera en interaction régulière avec les autres laboratoires et centres scientifiques membres du projet.

Des publications (et présentations, le cas échéant) en conférences et journaux internationaux sont attendues du (de la) post-doctorant(e).

 

Bibliographie : Bonastre, J. F., Kahn, J., Rossato, S., & Ajili, M. (2015). Forensic speaker recognition: Mirages and reality. In Speech Production and Perception: Speaker-Specific Behavior. hal-01473992.

 

DUREE :

 

18 mois. Début en janvier 2019.

 

PROFIL :

 

Vous êtes titulaire d’un doctorat en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la parole.

Vous possédez des connaissances en méthodologie d’évaluation et en biométrique vocale.

 

Pour candidater, merci d’envoyer votre CV et lettre de motivation à l’adresse recrut@lne.fr en rappelant la référence : ML/VOX/DE

 

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6-22(2018-10-09) 2 permanent positions at the European Language resources Distribution Agency (ELDA), Paris, France

The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill immediate vacancies for 2 permanent positions:

 

 

  • Web Developer position (m/f): Under the supervision of the technical department manager, the responsibilities of the Web Developer consist in designing and developing web applications and software tools for linguistic data management.
    Some of these software developments are carried out within the framework of European research and development projects and are published as free software.
    Depending on the profile, the Web Developer could also participate in the maintenance and upgrading of the current linguistic data processing toolchains, while being hands-on whenever required by the language resource production and management team.

  • Research and Development Engineer (m/f): Under the supervision of the CEO, the responsibilities of the R&D Engineer include designing, developing, documenting, deploying and maintaining tools, software components or applications for language resource production and management. He/she will be in charge of managing the current language resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded research and development projects.

Both positions based in Paris.

Please check the profile details for each open position here: http://www.elra.info/en/opportunities/

Contact: job@elda.org

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6-23(2018-10-08) Stage chez Airbus DS Elancourt, France
Une offre de stage chez Airbus DS Elancourt sur la reconnaissance automatique de la parole :
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6-24(2018-10-09) Stage Master Recherche,L'intelligibilité chez des patients atteints de troubles de la parole, Avignon, France

Stage Master Recherche – 6 mois Contact : Corinne Fredouille (corinne.fredouille@univ-avignon.fr)
Sujet : Approches à base de Deep Learning appliquées à l'évaluation de l'intelligibilité chez des patients atteints de troubles de la parole.
Le terme « troubles de la parole » fait référence à l'ensemble des déficiences affectant la production de la parole chez un être humain. Le bégaiement est un exemple de troubles de la parole.
 
Le LIA travaille depuis une dizaine d'années sur les troubles de la parole, et plus particulièrement sur la manière dont les outils de traitements automatiques peuvent aider les cliniciens et les phonéticiens dans leur analyse acoustico-phonétique du signal de parole et/ou perceptive des productions de parole en vue de mieux comprendre les dégradations inhérentes aux troubles de la parole. Les travaux les plus récents portent notamment sur l'étude d'un système de détection automatique de déviances dans des productions de parole dégradée [Laaridh et al., 2015] ou d'un système automatique de prédiction du degré d'intelligibilité basé sur des i-vecteurs [Laaridh et al., 2017 ; Laaridh et al., 2018]. Dans un premier temps, ces approches ont été appliquées sur des productions de parole dégradée produites par des patients atteints de lésions neurologiques localisées dans le système nerveux central ou périphérique. On parle alors de trouble moteur de la parole d'origine neurologique, désigné sous le terme de dysarthrie. La dysarthrie peut être l'un des symptômes de différentes maladies telles que la maladie de Parkinson, la Sclérose Latérale Amyotrophique (SLA), les Accidents Vasculaires Cérébraux, etc. Ces différentes maladies se distinguent notamment par la localisation des lésions neurologiques et, par conséquent, par le type de troubles moteur (faiblesse musculaire, mouvements involontaires, imprécision des mouvements …) et le type de dégradations de la parole qu'elles peuvent engendrer (distorsion des voyelles, imprécision des consonnes, altération du débit, hypernasalité, …). Ces approches ont été, dans un deuxième temps, évaluées sur des productions de parole dégradées issues de patients atteints de cancers des voies aérodigestives supérieures (présence de tumeurs) et/ou suite à des traitements thérapeutiques inhérents (exérèse, radiothérapie, etc).
En fonction de la maladie et de son évolution, les troubles moteur du patient pourront être évalués de manière perceptive (« à l'oreille ») par le clinicien sur une échelle de sévérité allant d'une dysarthrie légère à sévère. Sur une échelle similaire, le clinicien pourra également juger du degré d'intelligibilité de la parole d'un patient ie sa capacité à transmettre un message oral à un auditeur. Malgré son caractère très subjectif, l'évaluation perceptive des troubles de la parole chez les patients reste la seule approche à l'heure d'aujourd'hui utilisée en pratique clinique. Au vu des progrès observés dans le traitement automatique de la parole ces dix dernières années, des solutions technologiques sont âprement attendues dans ce domaine pour aider les cliniciens dans leur bilan clinique. Néanmoins, même si de nombreux travaux scientifiques portent sur l'utilisation d'approches automatiques pour une évaluation objective des troubles de la parole, un besoin de mieux comprendre les dégradations dans le signal de parole est nécessaire. Le LIA est engagé dans un projet financé par l'Agence Nationale de la Recherche (ANR) avec 3 autres partenaires sur la période 2019-2022 sur ce thème. L'un des objectifs de ce projet est de mieux comprendre quelles unités linguistiques sont majoritairement impliquées dans les processus d'intelligibilité du locuteur. Ainsi, une altération observée ou attendue de ces unités, conséquence d'une pathologie particulière, pourrait permettre de quantifier de manière objective la perte d'intelligibilité chez le patient. D'un point de vue pratique, il s'agira au travers des approches de Deep Learning et d'une comparaison parole normale/parole dégradée associée à des évaluations perceptives de l'intelligibilité d'aborder cette question. Le sujet du stage proposé ici s'inscrit dans ce cadre. Il aura pour objectif la mise en place du cadre expérimental nécessaire à la recherche des unités linguistiques impliquées dans ces processus d'intelligibilité. Il s'appuiera sur un état de l'art sur les approches de Deep Learning, qui devront être
vues non pas comme des boites noires mais comme un moyen d'extraire de l'information utile et de comprendre les processus étudiés : ici l'intelligibilité du locuteur.
A l'issue de ce stage, un financement de thèse (projet ANR) pourra être proposé au candidat.
Références bibliographiques
[Laaridh et al., 2015] I. Laaridh, C. Fredouille, C. Meunier, « Automatic Detection of Phone-Based Anomalies in Dysarthric Speech », ACM Transactions on Accessible Computing (TACCESS), (Volume : 6 Issue 3), June 2015.
[Laaridh et al., 2017] I. Laaridh, W. Ben-Kheder, C. Fredouille, C. Meunier, « Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech », Interspeech'2017, Stockholm, Sweden. August 2017.
[Laaridh et al., 2018] I. Laaridh, C. Fredouille, A. Ghio, M. Lalain, V. Woisard, « Automatic evaluation of speech intelligibility based on i-vectors in the context of Head and Neck Cancers », Interspeech'2018, Hyderabad, India. September 2018.

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6-25(2018-10-09) Stage Master Recherche: L' évaluation des troubles de la parole dans la maladie de Parkinson, Avignon, France

Stage Master Recherche – 6 mois Contact : Corinne Fredouille (corinne.fredouille@univ-avignon.fr)
Sujet  : Traitements automatiques appliqués à l'évaluation des troubles de la parole dans la maladie de Parkinson
Le terme « troubles de la parole » fait référence à l'ensemble des déficiences affectant la production de la parole chez un être humain. Le bégaiement est un exemple de troubles de la parole.
 
Le LIA travaille depuis une dizaine d'années sur les troubles de la parole, et plus particulièrement sur la manière dont les outils de traitements automatiques peuvent aider les cliniciens et les phonéticiens dans leur analyse acoustico-phonétique du signal de parole et/ou perceptive des productions de parole en vue de mieux comprendre les dégradations inhérentes aux troubles de la parole. Les travaux les plus récents portent notamment sur l'étude d'un système de détection automatique de déviances dans des productions de parole dégradée [Laaridh et al., 2015] ou d'un système automatique de prédiction du degré d'intelligibilité basé sur des i-vecteurs [Laaridh et al., 2017 ; Laaridh et al., 2018]. Dans un premier temps, ces approches ont été appliquées sur des productions de parole dégradée produites par des patients atteints de lésions neurologiques localisées dans le système nerveux central ou périphérique. On parle alors de trouble moteur de la parole d'origine neurologique, désigné sous le terme de dysarthrie. La dysarthrie peut être l'un des symptômes de différentes maladies telles que la maladie de Parkinson, la Sclérose Latérale Amyotrophique (SLA), les Accidents Vasculaires Cérébraux, etc. Ces différentes maladies se distinguent notamment par la localisation des lésions neurologiques et, par conséquent, par le type de troubles moteur (faiblesse musculaire, mouvements involontaires, imprécision des mouvements …) et le type de dégradations de la parole qu'elles peuvent engendrer (distorsion des voyelles, imprécision des consonnes, altération du débit, hypernasalité, …). Ces approches ont été, dans un deuxième temps, évaluées sur des productions de parole dégradées issues de patients atteints de cancers des voies aérodigestives supérieures (présence de tumeurs) et/ou suite à des traitements thérapeutiques inhérents (exérèse, radiothérapie, etc).
En fonction de la maladie et de son évolution, les troubles moteur du patient pourront être évalués perceptivement par le clinicien sur une échelle de sévérité allant d'une dysarthrie légère à sévère. Sur une échelle similaire, le clinicien pourra juger du degré d'intelligibilité de la parole d'un patient ie sa capacité à transmettre un message oral à un auditeur.
La revue de la littérature sur l'application des outils de traitement automatique de la parole dans le cadre de troubles de la parole montre que la communauté scientifique souffre d'un manque cruel de données cliniques disponibles. En effet, les systèmes automatiques nécessitent pour une majorité d'entre eux une quantité importante de données pour l'apprentissage des modèles sur lesquels ils reposent. La mise à disposition de grands corpus de parole normale (des centaines d'heures d'enregistrements accompagnées d'annotations manuelles) a d'ailleurs permis les améliorations de ces dernières années des systèmes de reconnaissance de la parole et leur utilisation dans le cadre d'applications grand public. Dans le contexte clinique, la constitution de corpus pose un certain nombre de problèmes : accès aux patients pour leur enregistrement au travers de collaborations avec des cliniciens de centres hospitaliers, demande d'autorisation de commissions hospitalières pour la mise en place d'un programme de recherche impliquant des patients, mise en place de protocoles particuliers prenant en compte les contraintes cliniques, collecte des données audio mais également de données cliniques nécessaires (informations personnelles du patient, pathologie, stade de progression, prise en charge thérapeutique, évaluation perceptive menée par les cliniciens, …) pour l'analyse des résultats.  Ces difficultés expliquent le nombre très restreint de corpus actuellement disponibles. Le LIA dispose, grâce à ces collaborations avec d'autres laboratoires de recherche et des établissements
hospitaliers de quelques corpus, comptabilisant un peu plus d'une centaine de locuteurs, enregistrés principalement sur une tâche de lecture d'un texte et pour quelques un sur de la parole spontanée. Néanmoins, cela reste insuffisant pour une application efficiente des outils de traitement de la parole.
Le Laboratoire Parole et Langage (LPL) d'Aix-en-Provence enregistre depuis de très nombreuses années des patients atteints de dysarthrie et de dysphonie (altération de la voix). Il a ainsi accumulé des enregistrements de plus de 2500 patients, dont 600 d'entre eux sont atteints de la maladie de Parkinson. Ces données audio sont accompagnées dans leur grande majorité des données cliniques du patient. Le LPL a fait un énorme travail depuis une dizaine d'années d'homogénéisation et de structuration de ces données au travers d'une base de données conséquente permettant le stockage et la pérennisation de ces données. L'objectif du LIA est à présent d'exploiter cette masse de données en se focalisant dans un premier temps sur les quelques 600 patients atteints de la maladie de Parkinson. Il s'agira dans le cadre de ce stage d'établir très rapidement une cartographie de ces patients, décrivant leur âge, genre, nombre d'enregistrements, quantité de parole disponibles, type de tâches, évaluations perceptives disponibles… Cette première phase très préliminaire permettra au candidat de se familiariser avec le contexte particulier des données cliniques. La deuxième phase du stage reposera sur une revue de la littérature faisant référence au Challenge COMPARE Interspeech 2015 [Schuller et al., 2015], dédié à la prédiction de l’état neurologique de patients atteints de la maladie de Parkinson sur la base de l’échelle UPDRS (Unified Parkinson's Disease Rating Scale). Cette revue permettra de faire état des différentes approches proposées dans ce contexte particulier  pour extraire de l'information pertinente pour la caractérisation de la dysarthrie chez des sujets atteints de la maladie de Parkinson et pour la prédiction de la sévérité de leur dysarthrie. Il s'agira pour le candidat d'évaluer le comportement de quelques unes de ces approches sur le corpus de patients à notre disposition voire d'en proposer de nouvelles le cas échéant. Une analyse par type de tâche proposé dans le protocole d'enregistrement pourra également faire partie du travail (lecture d'un texte, lecture de mots, répétition de syllabes, ...).
A l'issue de ce stage, un financement de thèse pourra être proposé au candidat.
Références bibliographiques
[Laaridh et al., 2015] I. Laaridh, C. Fredouille, C. Meunier, « Automatic Detection of Phone-Based Anomalies in Dysarthric Speech », ACM Transactions on Accessible Computing (TACCESS), (Volume : 6 Issue 3), June 2015.
[Laaridh et al., 2017] I. Laaridh, W. Ben-Kheder, C. Fredouille, C. Meunier, « Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech », Interspeech'2017, Stockholm, Sweden. August 2017.
[Laaridh et al., 2018] I. Laaridh, C. Fredouille, A. Ghio, M. Lalain, V. Woisard, « Automatic evaluation of speech intelligibility based on i-vectors in the context of Head and Neck Cancers », Interspeech'2018, Hyderabad, India. September 2018.
[Schuller et al., 2015] B. Schuller, S. Steidl, A. Batliner, S. Hantke, F. Hönig, J. R. OrozcoArroyave, E. Nöth, Y. Zhang, et F.Weninger, 2015. The interspeech 2015 computational paralinguistics challenge : Nativeness, Parkinson’s & eating condition. Proceedings of Interspeech’15, Dresden, Germany.

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6-26(2018-10-12) Maitre de conf'érence, Centrale Supelec Metz France

CentraleSupélec Metz recrute un MCF section 27 ou 61:
http://www.loria.fr/wp-content/uploads/2018/09/Profil-MCF-contractuel_DataScienceMathematics_03092018.pdf

Sur le plan de la recherche, 4 équipes sont ciblées dont la nôtre sur le traitement de la
parole (https://team.inria.fr/multispeech/).

Nous invitons les candidats potentiels à nous contacter.

Date limite de candidature: 5 novembre.

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6-27(2018-10-17) Tenure-Track Assistant Professor: Computational Linguistics, Rochester Institute of Technology, USA

Job: Tenure-Track Assistant Professor: Computational Linguistics, Rochester Institute of Technology

 

The Department of English at the Rochester Institute of Technology invites applications for a full-time, 9 month tenure-track Assistant Professor of Computational Linguistics/Linguistics, beginning in August 2019. Candidates are expected to have an earned doctorate in Linguistics, Computer Science, or a related field by the time of appointment. The committee will consider candidates who will finish their doctorate degree in the first year.

Successful candidates should demonstrate computational expertise, strong research talent, and initiative in grant writing. Candidates should also have a plan for effective teaching and student mentoring at the introductory and advanced undergraduate and graduate levels. We invite applicants to explore our curriculum with courses ranging from linguistic foundations and courses in core sub-disciplines of linguistics to natural language processing and speech processing.

We are especially interested in qualified candidates who will exhibit the ability to contribute in meaningful ways to the college's continuing commitment to cultural diversity, pluralism, and individual differences.

We are seeking an individual who has the ability and interest in contributing to a community committed to student centeredness; professional development and scholarship; integrity and ethics; respect, diversity and pluralism; innovation and flexibility; and teamwork and collaboration. Select to view links to RIT's http://www.rit.edu/academicaffairs/policiesmanual/p040http://www.rit.edu/academicaffairs/policiesmanual/p030, and https://www.rit.edu/academicaffairs/policiesmanual/p050

Required Minimum Qualifications

- An earned doctorate (PhD. or equivalent) in Linguistics, Computer Science, or a related field at time of appointment or within the first year;
- Demonstrated research excellence;
- Plan for developing a long-term research program including grant attainment;
- Potential for effective teaching and mentoring of undergraduate and graduate students;
- Ability to contribute in meaningful ways to the college?s continuing commitment to cultural diversity, pluralism, and individual differences.

Apply online at http://careers.rit.edu/faculty  Keyword Search: 4119BR


Please submit: your curriculum vitae, cover letter addressing the listed qualifications and upload the following attachments:
- Research statement (not to exceed 2 pages)
- Teaching statement (not to exceed 1 page)
- Two sample publications or research products (URL acceptable)
- The names, email addresses, and phone numbers for three references
- Contribution to Diversity Statement

You can contact the search committee chair Dr. Cecilia O. Alm with questions on the position at coagla@rit.edu.

Review of applications will begin on November 27, 2018 and will continue until a suitable candidate is found.

The direct link to this posting can be found here: https://apptrkr.com/1315291

Additional Details 

 

RIT does not discriminate. RIT is an equal opportunity employer that promotes and values diversity, pluralism, and inclusion.  For more information or inquiries, please visit RIT/TitleIX or the U.S. Department of Education at ED.Gov

 

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6-28(2018-10-19) post-doctoral researcher , University of Twente, The Netherlands

In the context of the ZonMW project Smart Sports Exercises, the University of Twente is looking for a post-doctoral researcher to develop new multimodal, interactive, digitally enhanced training exercises on an instrumented floor with actuators (LED video screen) and sensors that offer minimized on-body motion sensing. The successful candidate will be embedded in the Human Media Interaction group, and will work together with a colleague already employed at the Biomedical Signals and Systems group who develops new models and methods for tracking and analyzing behavioral data of volleyball players.

 

As a successful applicant, you will be working in a highly interdisciplinary research project. The aim of the project is to enhance volleyball training and trainer education through three main lines of innovation: (1) development of new interactive concepts for digitally supported training exercises, using game like interaction on an instrumented LED video floor, (2) innovation of data science for automatic analysis of behavioural data in sports exercises to provide the necessary input, and (3) innovation in conceptual thinking about sport training and PE that combines technological and non-technological elements, incorporating pedagogy and training didactics. We target a range of users, from elite players to youth and recreational teams, and including players, trainers, and teacher educators.

 

The project is a collaboration between a consortium of research institutes, sports organisations, and companies. The University of Twente leads the project, and is responsible for the data science and interaction technology in the project. Other partners are the Windesheim University of Applied Sciences (CALO), InnoSportLab Sport & Beweeg, Sportservice Veenendaal, and LedGo BV.

 

In this particular open position, you will work on developing interactive training exercise systems and on evaluating user experience, perception and performance in explorative and experimental studies. This will be done iteratively, in close interaction with end users such as volleyball trainers and players.

 

 

Your profile

 

You are passionate to work one of the above topics, and have clearly relevant background for that. You hold a PhD degree in computer science, biomedical engineering, HCI, game design or other relevant domain. You are capable of designing and realizing interaction technology systems. An additional background in user centered research and design, games, or embodied interaction is considered a pre.

 

You are an independent and self-directed researcher and developer, but also a team player able to work in a diverse and multidisciplinary consortium. You are an excellent researcher as well as someone who can contribute to the realization of actual interactive technology systems.

 

More information on this position

How to apply

Apply to this position by submitting at the following link (closing date: October 31st):

https://www.utwente.nl/en/organization/careers/vacancy/!/462306/

Your application should include the following documents:

  • a cover letter (in English or Dutch) which explains your interest in the position and your qualifications.
  • a curriculum vitae which also includes the name and e-mail address/telephone number for (preferably) two or more references;
  • a copy of your PhD-thesis or, if it is not yet available, an outline and summary of your thesis and one of your scientific papers.

 

Our offer

 

A challenging opportunity to perform post-doctoral research in the context of a highly ambitious 2-year ZonMw-funded project. The University of Twente offers a stimulating work environment in an area of applied, forefront research and offers strong and inspiring collaboration possibilities with the medical field. You will have a 0.8-1.0 fte position for the duration of ~2 years and can participate in all employee benefits the UT offers. The gross monthly salary starts with ? 3111,- in the first year and increases over time. Additionally, the University of Twente provides excellent facilities for professional and personal development, a holiday allowance of 8%, and an end of year bonus of 8.3%.

 

The organization

 

The University of Twente. We stand for life sciences and technology. High tech and human touch. Education and research that matter. New technology which leads change, innovation and progress in society. The University of Twente is the only campus university of the Netherlands; divided over five faculties we provide more than fifty educational programmes. We have a strong focus on personal development and talented researchers are given scope for carrying out groundbreaking research.

 

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6-29(2018-10-16) Postdoctoral ASR, Radbout University, Nijmegen, The Netherlands (updated)

Radbout University, Nijmegen, The NetherlandsApplication deadline: 31 January 2019

 
Vacancy: Postdoctoral ASR ? Speech Researcher
on the project BLISS: Behaviour-based Language-Interactive Speaking Systems

The Centre of Language and Speech Technology (CLST) at Radboud University Nijmegen has a vacancy for a post-doctoral speech technology researcher. BLISS [http://hstrik.ruhosting.nl/bliss/] combines research on how using Big Data and information extraction can make a substantial contribution to self-management and improvement of the individual health situation. In this project we will build a smart, personalized, spoken dialogue system (SDS) supporting self-management that is able to meaningfully communicate with people about issues regarding their personal health, well-being and happiness using Automatic Speech Recognition (ASR). For this SDS we need to personalise the ASR component to adapt to speaker variation and to topic changes in dialogue. As a researcher you will combine knowledge of Deep Neural Networks (DNNs, Kaldi) based ASR with experience in SDS.

For more information: http://hstrik.ruhosting.nl/asr-postdoc-bliss/
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6-30(2018-10-16) Postdoctoral researcher, Radboud Univdersity, Nijmegen, The Netherlands

Application deadline: 14 November 2018

Vacancy ? job opening: Postdoctoral researcher [
http://hstrik.ruhosting.nl/postdoc-reading-dart/]
on the project DART: Dutch ASR-based Reading Tutor [
http://hstrik.ruhosting.nl/DART/]

Literacy is a prerequisite to participate in our knowledge society and reading skills are essential for school success. However, developing reading skills requires intensive practice. To facilitate this process we will develop educational software that incorporates Automatic Speech Recognition (ASR) to provide instantaneous, automated feedback on reading aloud. The research will first address how the ASR-based reading software can best be designed and implemented in the school practice and at home. Educational software with optimized ASR technology and different forms of feedback will then be developed and tested in realistic conditions. This research will provide important theoretical and practical insights on the possibilities and effectiveness of ASR-based reading software.

For more information, see
http://hstrik.ruhosting.nl/postdoc-reading-dart/

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6-31(2018-10-16) Junior ASR Researcher, Radboud University, Nijmegen, The Netherlands

Application deadline: 14 November 2018

Vacancy ? job opening: Junior ASR - Speech
researcher[http://hstrik.ruhosting.nl/asr-junior-dart/]
on the project DART: Dutch ASR-based Reading Tutor
[http://hstrik.ruhosting.nl/DART/]

Literacy is a prerequisite to participate in our knowledge society and
reading skills are essential for school success. However, developing
reading skills requires intensive practice. To facilitate this process
we will develop educational software that incorporates Automatic Speech
Recognition (ASR) to provide instantaneous, automated feedback on
reading aloud. The research will first address how the ASR-based reading
software can best be designed and implemented in the school practice and
at home. Educational software with optimized ASR technology and
different forms of feedback will then be developed and tested in
realistic conditions. This research will provide important theoretical
and practical insights on the possibilities and effectiveness of
ASR-based reading software.

For more information, see http://hstrik.ruhosting.nl/asr-junior-dart/

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6-32(2018-10-18) Post-doc 2 years, Aalto University, Finland

Aalto University, Department of Signal Processing and Acoustics (Finland) invites applications for
 
Postdoc position in Speech Processing
 
The Department of Signal Processing and Acoustics is a part of School of Electrical Engineering at Aalto University (Finland). The department consists of four main research areas. The speech communication technology research group (led by Prof. Paavo Alku) works on interdisciplinary topics aiming at describing, explaining and reproducing communication by speech. The main topics of our research are speech production, particularly glottal source analysis, speech parameterization, speaking style conversion, and statistical parametric speech synthesis. 
We are currently looking for a postdoc to join our research team to work on the team’s research themes. We are particularly interested in candidates with research interest in paralinguistic speech processing, especially related to human health, and in candidates with a background in machine learning –based acoustic-to-acoustic conversion (e.g. voice conversion). We also welcome strong candidates from other areas but previous experience in speech technology research is a must. 
Postdoc: 2 years. Starting date: January-May 2019  
In Helsinki you will join the innovative international computational data analysis and ICT community. Among European cities, Helsinki is special in being clean, safe, Scandinavian, and close to nature, in short, having a high standard of living. English is spoken everywhere. See, e.g., http://www.visitfinland.com/
Requirements The position requires doctoral degree in speech technology, computer science, signal processing or other relevant area, skills for doing excellent research in a group, and outstanding research experience in any of the research themes mentioned above. The candidate should have a strong background in machine learning and/or signal processing and previous experience in speech research. The candidate is expected to perform high-quality research and assist in supervising PhD students.
 
How to apply If you are interested in this opportunity, apply by submitting the following documents in English and in electrical form (use the pdf format only!) by December 31, 2018. Send your application, CV, a transcript of academic records and references directly by email to Professor Paavo Alku. Please insert the subject line “Aalto postdoc recruitment, 2018”.
 
Additional information Paavo Alku, paavo.alku@aalto.fi 
 

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6-33(2018-10-20) POSTDOCTORAL FELLOWSHIP , UNIVERSITY OF LORRAINE, Nancy, France


POSTDOCTORAL FELLOWSHIP 
OPEN POSITION
UNIVERSITY OF LORRAINE
 
TITLE Online Hate Speech Against Migrants
 
COORDINATION Research coordinator: Crem, University of Lorraine (Pr. Angeliki Monnier) Co-coordinator: Loria, University of Lorraine (Dr. Irina Illina MCF HDR, Dr. Dominique Fohr CNRS)
 
TERMS AND VENUE This one-year position will be based at Crem (Metz/Nancy) and Loria (Nancy), France (University of Lorraine). The target start date for the position is April 1st, 2019, with some flexibility on the exact start date.
 
SELECTION CRITERIA PhD in Information and Communication Sciences, or Language Science. Research experience in social media and language data analysis. Very good mastery of the French language. Very good written and oral expression skills, in French and in English. Curiosity, open-mindedness. Autonomy, teamwork skills.
 
HOW TO APPLY  Applicants are requested to submit the following materials: • A cover letter applying for the position • Full CV and list of publications • Statement of Research (summary of research achievements and perspectives for upcoming researches) • Academic transcripts (diplomas, courses statements, grades, etc.) (unofficial versions are fine) • Recommendation letters are not obligatory but are strongly recommended.
 
Deadline for application is December 10th, 2018.  Applications are only accepted through email. All documents must be sent to angeliki.monnier@univ-lorraine.fr
 
Applicants will be interviewed by an Ad Hoc Commission on January 15th, 2019.
 
DETAILED DESCRIPTION OF THE RESEARCH PROJECT
This position is open as part of the Open Language and Knowledge project for Citizens (OLKi), carried out within the IMPACT/Lorraine University of Excellence (LUE) framework. Under the direction of 

 
 
 
 
2
 
Pr. Angeliki Monnier (Crem), Dr. Irina Illina and Dr. Dominique Fohr (Loria), the post-doctoral fellow will work on online hate speech against migrants.
Social context
According to the 2017 International Migration Report, the number of migrants worldwide has increased rapidly in recent years. This development is causing great public concern around the world, particularly in Europe. The economic crisis affecting some countries of the Old Continent also feeds feelings of insecurity, encouraging the development of anti-immigrant movements. The media are often pointed out for their tendency to depict refugees and migrants negatively, consolidating fears. A recent EU project has revealed a significant increase in hate speech against immigrants and minorities, who are often accused of being the cause of current economic and social problems. Participatory web and social media seem to amplify the intensity and scope of hate speech. The fight against racism and hatred on the Internet is currently one of the priorities of the French government. On September 20th, 2018, a report commissioned on this topic was given to the Prime Minister, containing twenty proposals to combat hate on the Internet. 
Scientific context and scope
The objective of this postdoctoral contract will be to study the context of the appearance of hateful contents (circumstances of emergence, locutors, dissemination processes, etc.), and to analyze the latter as linguistic productions (narrative approaches, speech acts, enunciation, etc.) in the light of the creation of a lexicon of hate speech in French.
Crem and Loria are already involved in this project. Owing to the technology of “neural networks” (deep learning), their collaboration aims to collect and shape a corpus of hateful expressions against migrants, but also to develop an app that could automatically detect hate speech in comments posted on the Internet, especially in media websites.
The objective of the Crem-Loria collaboration in this postdoctoral contract is to refine these initial results through qualitative analyses of online hate speech against migrants. The aim is to achieve a better understanding of the social phenomenon of hatred, as well as to improve the development of algorithms used to qualify language.
For this reason, the collaboration between Crem and Loria will take an iterative form, between the qualitative analysis of restricted corpora and the work with algorithms. It will cover both the constitution of the corpus (online data collection, search for expressions of hate) and the analysis of this corpus (creation and organization of the lexicon).
The research will focus on user-generated content (social networks, comments on media websites, etc.). Part of the analyzed data will come from the OLKi platform, which will also serve as a support for the evaluation of the algorithms developed.
 
FELLOW’S MISSIONS • Collect online data (hate speech against migrants), using technical solutions proposed by Loria. • Analyze these discourses using Humanities and Social Sciences (SHS) approaches: sociopragmatic contexts of hateful comments, linguistic analysis of speeches (speech acts, narratives, enunciation, etc.). Depending on the candidate’s profile, image analysis can be included in the research project, as an additional component. • Contribute to the development of a lexicon in French about online hatred against migrants (supervised by Crem and Loria).

 
 
 
 
3
 
• Write scientific articles based on research results, in collaboration with the supervisors and cosigned with them, to be published in scientific journals and / or to be presented at national and / or international conferences. • Attend regular meetings between the two teams. • Provide regular reports on the project’s progress and a final report at the end of it. • The fellow is expected to regularly participate in the Crem and Loria seminars and other research activities.
 
AFFILIATION UNITS
Crem, Center for Research on Mediations, comprises more than 230 researchers: approximately 80 tenured scholars, more than 90 doctoral students, 45 associate members and 7 staff members. Its researchers belong to 11 disciplines: nearly 90 % come from the Information and Communication Sciences, Language sciences, French, Literature and Art Sciences; about 10 % are specialists in English and Anglo-Saxon languages, Arabic, Germanic and Romance languages, Anthropology, Psychology and Sociology.
Pixel is one of the four Crem teams, with more than 40 members, including 16 tenured scholars, with a specialization in the field of the usages of information and communication technologies. Pixel researchers implement different methodological approaches: surveys, content analyzes, sociotechnical analyzes, usage observations, socio-historical analysis, etc. For years, Pixel has been developing a sustained research activity around several thematic areas: digital educational practices (online learning platforms, serious games), access to online information (search engines, information websites, social networks, micro-blogging, information monitoring), online collaboration (participatory work environments, watch and curation tools, viral dissemination of journalistic content) and creative industries (digital games, expressive games, video games). Website: https://crem.univ-lorraine.fr/lunite/equipe-pixel/ 
Loria, Laboratory of Research in Computer Science and its Applications is a joint research unit (UMR 7503), common to several institutions: the CNRS, the University of Lorraine and Inria. Since its creation in 1997, Loria's mission is to enhance and promote fundamental and applied research in Computer Sciences. The scientific work is carried out in 28 teams structured in 5 departments, 15 of which are shared with Inria, representing a total of more than 400 persons. Loria is one of the largest laboratories in Lorraine.
Multispeech is one of the 28 Loria-INRIA Grand Est teams. It comprises 12 tenured scholars. The Multispeech research project focuses on speech processing, paying particular attention to multisource (source separation, robust speech recognition), multilingual (foreign language learning) and multimodal (audiovisual synthesis) aspects. Its research program is structured in 3 axes: - the explicit modeling of speech, which exploits its physical dimension; - the statistical modeling of speech, which relies on techniques of automatic learning such as the Bayesian models (HMM-GMM) and networks of deep neurons (DNN); - the uncertainties related to the high variability of the speech signal and the imperfection of the models. Website: http://team.inria.fr/multispeech/

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6-34(2018-11-02) Funded PhD position, University of Glasgow, UK

The University of Glasgow is seeking applications for the following funded PhD position in the area of Explainable AI, Planning, and Human-Machine Interaction. Interested students should apply as soon as possible

 

Eligibility: UK/EU students only

Start date: 1 January 2019

Full details at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=98605  

 

Many future industrial operations will be carried out by teams consisting of humans and machines. In this project, the student will investigate how human-machine trust and explainable/transparent artificial intelligence affect such human-machine collaborative tasks. The work will concentrate on the communication aspects: how the machine communicates its intentions and reasoning processes to the human, and how the human can query and interact with the robot’s plan.

 

The project will be driven by oilfield drilling applications, which involve control of complex equipment in a dynamic environment, with an increasing level of automation. In this setting, close coordination and trust between the human crew and the automation system is required: the crew must both understand why the machine acts the way it does, as well as be confident it has taken all available information into account.

 

The student should have excellent experience, enthusiasm and skills in the areas of artificial intelligence and/or automated planning and reasoning and/or natural language or multimodal interaction. Applicants must hold a good Bachelor’s or Master’s degree in a relevant discipline.

 

The project is an EPSRC iCASE award with Schlumberger Gould Research and it is expected that the student will spend some time working with the company in Cambridge. This will give you a great opportunity of working in an internationally excellent research group as well as a leading player in the oil and gas industry. Travel and accommodation costs will be paid during the time at Schlumberger.

 

For more information, please contact Dr Mary Ellen Foster MaryEllen.Foster@glasgow.ac.uk. Feel free to contact me directly if you have questions, but only applications received through the University of Glasgow (as linked from the FindAPHD page) can be considered officially.

 

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6-35(2018-11-04) Tenure positions at University of Santa Cruz, California, USA

UNIVERSITY OF CALIFORNIA, SANTA CRUZ DEPARTMENT OF Computer Science and Engineering Assistant and Associate or Full Professor, Natural Language Processing 


 The Department of Computer Science and Engineering at the University of California, Santa Cruz invites applications for two positions in the field of Natural Language Processing. One position is at the tenured Associate or early stage Full Professor level, and the other position is at the tenure track Assistant Professor level. We seek outstanding applicants with research and teaching expertise in all areas of Natural Language Processing. We are especially interested in candidates who have contributed to one or more application areas of Natural Language Processing including but not limited to information extraction, dialogue systems, semantic parsing, sentiment analysis, question answering, and machine translation.  
 Both positions are associated with a proposed Professional MS program in Natural Language Processing to be located in the UCSC Silicon Valley Campus in Santa Clara, California. The successful candidates will play an essential role in developing, growing, and shaping this new program. They are expected to develop a research program, advise Ph.D. students in their research area, obtain external funding, develop and teach courses within the undergraduate and graduate curriculum, perform university, public, and professional service, and interact broadly with the large number of Natural Language Processing practitioners in Silicon Valley industrial research and advanced development labs. The successful candidates should be able to work with students, faculty and staff from a wide range of social and cultural backgrounds. In addition to the basic qualifications, applicants at the Associate or Full Professor level should have a demonstrated record of publications, demonstrated experience in university teaching at the undergraduate and graduate level or closely analogous activities, demonstrated record of extramural funding or similar success with garnering support for research endeavors, experience with research project management, and professional service; we also value industrial experience, and a track record of building product and applications based on NLP technology.
 We welcome candidates who understand the barriers facing women and minorities who are underrepresented in higher education careers (as evidenced by life experiences and educational background), and who have experience in equity and diversity with respect to teaching, mentoring, research, life experiences, or service towards building an equitable and diverse scholarly environment.
 The primary offices for these positions are located in Santa Clara, due to the expectation of teaching and mentoring students in this location. Space for PhD students for these positions is also located in Santa Clara. Graduate level teaching duties will be mainly at the Santa Clara campus with undergraduate courses to be taught at the Santa Cruz campus.  The successful applicants will typically spend multiple days per week in Santa Clara and are also expected to spend on average one day per week on the Santa Cruz campus (more when teaching an undergraduate class on the Santa Cruz campus). The ability for ondemand transportation between Santa Clara and Santa Cruz with or without accommodations is essential.
 The Computer Science and Engineering Department has nationally and internationally known research groups in Machine Learning, Data Science, Natural Language Processing and related fields. Our beautiful campus has a long history of embracing groundbreaking interdisciplinary work, and the proximity of the campus to Silicon Valley affords our faculty extensive opportunities for interactions and collaborations with industry. 
 ACADEMIC TITLES Assistant Professor and Associate or early stage Full Professor
 SALARY Commensurate with qualifications and experience; academic year (9-month basis).
 BASIC QUALIFICATIONS A Ph.D. or equivalent foreign degree in Computer Science or a relevant field expected to be completed by June 30, 2019; demonstrated record of research and teaching.

 POSITION AVAILABLE July 1, 2019 (with academic year beginning September 2019). Degree must be in hand by June 30, 2019. 
 APPLICATION REQUIREMENTS Applications are accepted via the UCSC Academic Recruit online system; all documents and materials must be submitted as PDFs. 
 APPLY AT https://recruit.ucsc.edu/apply/JPF00657  Please refer to Position # JPF00657-19 in all correspondence. 
 Documents/Materials  • Letter of application that briefly summarizes your qualifications and interest in the position  • Curriculum vitae • Statement addressing contributions to diversity through research, teaching, and/or service (required). Guidelines on diversity statements can be viewed at https://senate.ucsc.edu/committees/caad-committee-on-affirmative-action-anddiversity/DivStateGuidelines.pdf. • Statement of research plans  • Statement of teaching interests and experience • 3–4 selected publications • 3 confidential letters of recommendation*
 Please note that your references, or dossier service, will submit their confidential letters directly to the UC Recruit System.
 *All letters will be treated as confidential per University of California policy and California state law. For any reference letter provided via a third party (i.e., dossier service, career center), direct the author to UCSC’s confidentiality statement at http://apo.ucsc.edu/confstm.htm.
 RECRUITMENT PERIOD Full consideration will be given to applications completed by December 3rd, 2018. Applications received after this date will be considered only if the position has not been filled.
 
UC Santa Cruz faculty make significant contributions to the body of research that has earned the University of California the ranking as the foremost public higher education institution in the world. In the process, our faculty demonstrate that cutting-edge research, excellent teaching and outstanding service are mutually supportive.
 The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. UC Santa Cruz is committed to excellence through diversity and strives to establish a climate that welcomes, celebrates, and promotes respect for the contributions of all students and employees. Inquiries regarding the University’s equal employment opportunity policies may be directed to the Office for Diversity, Equity, and Inclusion at the University of California, Santa Cruz, CA 95064 or by phone at (831) 459-2686.
 Under Federal law, the University of California may employ only individuals who are legally able to work in the United States as established by providing documents as specified in the Immigration Reform and Control Act of 1986. Certain UCSC positions funded by federal contracts or sub-contracts require the selected candidate to pass an E-Verify check (see https://www.uscis.gov/e-verify). More information is available at the APO website (see https://apo.ucsc.edu/policy/capm/104.000%20.html) or call (831) 459-4300.
 UCSC is a smoke & tobacco-free campus.  
 If you need accommodation due to a disability, please contact the Academic Personnel Office at apo@ucsc.edu (831) 459-4300.  VISIT THE APO WEB SITE AT http://apo.ucsc.edu [10/2/2018]

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6-36(2018-11-06) Stage au LIMSI, Orsay, France

# Intitulé

Comparaison de fonctions objectif pour l?apprentissage de représentation :
application à la vérification du locuteur et au calcul de similarité sémantique textuelle

# Résumé

Le rôle de la fonction objectif dans l?apprentissage neuronal est de fournir une mesure de la performance du réseau de neurones (i.e. sa capacité à répondre correctement à une tâche précise). Cette mesure, lorsqu?elle est dérivable, permet alors de mettre à jour le réseau de neurones par rétro-propagation du gradient de telle sorte que sa performance soit améliorée. Parmi ces fonctions objectif, on peut par exemple citer la ?contrastive loss? [HCL06], la ?triplet loss? [SKP15], ou encore la ?center loss? [WZLQ16]. L?objectif de ce stage est de comparer différentes fonctions objectif permettant l?apprentissage des représentations neuronales adaptées à des tâches applicatives telles que la vérification du locuteur et la similarité sémantique textuelle. La plupart de ces méthodes ont été initialement proposées dans le domaine de la vision par ordinateur pour la reconnaissance d?image (et de visage en particulier) et certaines ont !
 été appliquées récemment à tâche de vérification du locuteur [Bre17]. Cependant, elles n?ont pas encore été utilisées pour la tâche de similarité sémantique textuelle.

# Description des tâches

* Implémentation des différentes fonctions objectif
Après une étape d?étude de la littérature sur le sujet, la première tâche consiste à implémenter les fonctions objectif les plus prometteuses en les testant sur des exemples jouet bien maîtrisés (tels que la base MNIST de reconnaissance de chiffre manuscrit, par exemple).

* Application à la vérification du locuteur
La tâche de vérification du locuteur consiste à déterminer si deux signaux audio proviennent ou non de l?enregistrement du même locuteur. On utilisera la base de données VoxCeleb [CNZ18, NCZ17] pour mener ces expériences. Elle contient plus d?un million d?enregistrements correspondant à plus de 6000 locuteurs, et constitue de fait le plus grand corpus librement disponible pour l?identification et la vérification du locuteur.

* Application au calcul de similarité sémantique textuelle
La tâche de similarité sémantique textuelle (SST) est motivée par le fait que la modélisation de la similarité sémantique des phrases est un problème fondamental en compréhension de la langue, pertinent pour de nombreuses applications, notamment la traduction automatique, la recherche de réponses à des questions précises (ou questions-réponses), le dialogue dialogue, etc. Cette tâche consiste à évaluer dans quelle mesure deux phrases sont sémantiquement équivalentes. Plusieurs approches ont étés proposées [CDA + 17], qui sont fondées généralement soit sur les méthodes classiques en traitement automatique des langues (TAL), soit sur des méthodes d?apprentissage profond. La première approche s?appuie sur l?utilisation d?un classifieur enrichi par différents types de descripteurs : sémantiques, syntaxiques, etc. La deuxième est fondée sur l?exploitation des représentations de phrases et des architectures neuronales. Dans le cadre des ca!
 mpagnes d?évaluation SemEval, la tâche de SST a été proposée. Dans ce cadre, la tâche consiste pour le système de SST à attribuer un score de similarité à chaque paire de phrase sur une échelle de 0 (les deux phrases sont complètement différentes) à 5 (les deux phrases sont complè tement identiques)? Notre objectif dans ce stage est de pouvoir étudier les différentes fonctions objectif sur la tâche SST et de comparer nos résultats aux résultats obtenus par les différents systèmes ayant participé à la tâche 5 (en anglais) de la campagne d?évaluation SemEval 2017. Ce système fait la combinaison des approches de TAL et d?apprentissage profond.

# Profil attendu

* Master 2 en Informatique (ou e?quivalent), avec au moins une spe?cialite? en apprentissage, traitement automatique de la langue, et/ou traitement de la parole.
* Compe?tence techniques : python, linux

# Informations pratiques

* Dure?e du stage : 5-6 mois (stage pouvant donner lieu a? une poursuite en the?se)
* De?but du stage : date de de?but a? de?finir avec le stagiaire
* Gratification : environ 570? par mois. remboursement frais de transport et subvention cantine

# Références

[Bre17] Hervé Bredin. Tristounet : triplet loss for speaker turn embedding. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5430?5434. IEEE, 2017.

[CDA + 17] Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. Semeval-2017 task 1 : Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv :1708.00055, 2017.

[CNZ18] Joon Son Chung, Arsha Nagr ni, and Andrew Zisserman. Voxceleb2 : Deep speaker recognition. arXiv preprint arXiv :1806.05622, 2018.

[HCL06] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR 2006, pages 1735?1742. IEEE, 2006.

[NCZ17] Arsha Nagrani, Joon Son Chung, and Andrew Zisserman. Voxceleb : a large-scale speaker identification dataset. arXiv preprint arXiv :1706.08612, 2017.

[SKP15] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet : A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815?823, 2015.

[WZLQ16] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision, pages 499?515. Springer, 2016.

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6-37(2018-11-05) PhD student ?Morphology in spoken word recognition models? Radboud University, Nijmegen, The Netherlands

PhD student ?Morphology in spoken word recognition models?

Location:       Radboud University, Nijmegen

Duration:       4 years

Starting date:  February 2019

Starting salary:   Around 1600 euros a month

Supervision:   
The PhD project will be supervised by Louis ten Bosch, Mirjam Ernestus, and Ingo Plag .
The project is part of the ?Spoken Morphology? research unit (http://www.spoken-morphology.hhu.de/en.html)

Requirements:   
We are looking for candidates with
?       Master degree in Linguistics or in Artificial Intelligence;
?       Clear interest in speech and language;
?       Expertise in qualitative and/or quantitative research methods, preferably including large scale data analyses and statistical analyses;
?       Effective verbal and written communicative skills in English.

Application:   
Please send your motivation letter and extensive resume (including grades for all university courses) before 1 December 2018  to m.ernestus@let.ru.nl

Questions:             
l.tenbosch@let.ru.nl or m.ernestus@let.ru.nl

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6-38(2018-11-11) Research engineer or post-doc position in Natural Language Processing at LORIA-INRIA, Nancy, France

Research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system

 

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA (https://team.inria.fr/multispeech/)

Contact: illina@loria.fr, dominique.fohr@loria.fr

Duration: 12-15 months

Deadline to apply : December 20th, 2019

Required skills: Strong background in mathematics, machine learning (DNN), statistics, natural language processing and computer program skills (Perl, Python).

Following profiles are welcome, either:

  • Strong background in signal processing

or

  • Strong experience with natural language processing

Excellent English writing and speaking skills are required in any case. 

Candidates should email a detailed CV with diploma

 

LORIA is the French acronym for the ?Lorraine Research Laboratory in Computer Science and its Applications? and is a research unit (UMR 7503), common to CNRS, the University of Lorraine and INRIA. This unit was officially created in 1997. Loria?s missions mainly deal with fundamental and applied research in computer sciences.

 

MULTISPEECH is a joint research team between the Université of Lorraine, Inria, and CNRS. Its research focuses on speech processing, with particular emphasis to multisource (source separation, robust speech recognition), multilingual (computer assisted language learning), and multimodal aspects (audiovisual synthesis).

 

Context and objectives

 

Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech.

 The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN.

The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.

 

Main activities

 

? study and implementation of a noisy speech enhancement module and a propagation of uncertainty module;

? design a semantic analysis module;

? design a module taking into account the semantic and uncertainty information.

 

References

 

 [Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018.

[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017.

[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016.

 [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013.

 

 

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6-39Postdoctoral Fellowship in Collaborative Social Robotics, KTH, Stockholm, Sweden

Postdoctoral Fellowship in Collaborative Social Robotics

 

Department of Speech, Music and Hearing, KTH
https://www.kth.se/tmh

 

Department of Robotics, Perception and Learning, KTH
https://www.kth.se/rpl

 

Specialty Areas: 

Social Robotics, Multimodal dialogue systems, HRI

 

Description:
KTH is recruiting a postdoctoral researcher in collaborative social robotics. The position is for 24 months (full time). The position is funded though the Factories of the future (FACT) project, a 5-year project jointly run by RPL and TMH. Within the FACT project, robots are developed that can assist people working in the assembly in domestic and industrial settings. More information can be found here:

http://www.speech.kth.se/~jocke/fact_flyer.pdf
http://factssf.se/

 

We are now looking for two postdocs to conduct research and development work within the project in one of the following areas

- Development of modules for controlling two-armed robots, such as YuMi.
- Human-robot interaction studies with YuMi and the social robot Furhat

An important part of the work is to publish scientific articles at highly rated international conferences and journals in robotics and HRI.


Qualifications
We are looking for people who have experience in robot programming and have documented experience of conducting advanced human-robot interaction studies. Applicants must hold or be about to receive a doctoral degree in a subject relevant to the research, such as automatic control, computer science, speech technology or cognitive science. The doctoral degree must have been obtained within the last three years from the application deadline (some exceptions for special grounds, for instance, sick leave and parental leave). Applicants should have a strong background in at least one of the following areas of robotics, speech technology or computer science. The position requires good knowledge of programming, machine learning, and statistical analysis. The successful applicant should have an outstanding research and publication record. Well-developed analytical and problem-solving skills are a requirement. We are looking for a strongly motivated person, who is able to work independently. Good command of English orally and in writing is required to present and publish research results. 


Research Environment:

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

 

Apply for the position here:

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:232546/where:4/

 

Application Deadline:
Nov 25, 2018 


Contacts for applications: 

Danica Kragic and Joakim Gustafson

 

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6-40(2018-11-13) Associate Linguist [Français] job offer, Paris, France

Associate Linguist [Français] job offer

 

Intitulé du poste :

Associate Linguist [French]

Champs linguistiques  :

Phonétique, Phonologie, Morphologie, Sémantique, Syntaxe, Lexicographie, TAL

Lieu :

Paris, France

Description du poste :

En tant qu’Associate Linguist, vous annoterez et réviserez des données linguistiques en français.  L’Associate Linguist contribuera également à un certain nombre de tâches en traitement automatique des langues, dont :

  • Transcription phonétique/phonémique d’entrées lexicales
  • Analyse de données acoustiques pour évaluer la synthèse vocale
  • Annotation et révision de données linguistiques
  • Labellisation de textes, désambiguisation, expansion, and normalisation des données
  • Annotation d’entrées lexicales en respectant les codes de référence
  • Evaluation des outputs système
  • Dérivation de données en TAL
  • Capacité à travailler de manière indépendante avec précision

Compétences requises:

  • Locuteur de langue maternelle française, parfaite maîtrise de l’anglais
  • Connaissance en transcriptions phonétiques et phonologiques
  • Familiarité avec les techniques et outils de synthèse de la parole et de reconnaissance vocale
  • Expérience en annotation
  • Connaissances en phonétique, phonologie, sémantique, syntaxe, morphologie et lexicographie
  • Excellentes compétences en communication orale et écrite
  • Attention aux détails et compétences organisationnelles 

 

Compétences désirées :

  • Diplôme en linguistique théorique et computationnelle et TAL
  • Capacité à saisir rapidement les concepts techniques et les outils conçus en interne
  • Vif intérêt pour la technologie et compétences en informatique
  • Compétences en écoute de données orales
  • Compétences en saisie de clavier rapide et précise
  • Familiarité avec les logiciels de transcription
  • Compétences en édition, correction grammaticale et orthographique
  • Compétences en recherche

 

CV + lettre de motivation en Anglais : maroussia.houimli@adeccooutsourcing.fr

2730E brut/mensuel + 50% Pass Navigo + Mutuelle

 

 

 

Maroussia HOUIMLI

Responsable recrutement

 

E maroussia.houimli@adeccooutsourcing.fr

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6-41(2018-11-15) Student intership, LIG Lab, Grenoble, France

Neural coreference resolution

Coreference resolution aims at detecting chains of coreference mentions in a text, that is mentions in the text that refer to the same entity.

While at first coreference resolution was split into two separated sub-problems, i.e. mention detections and resolution of coreferent mentions [1], thanks to the development of sophisticated neural models [2,3,4], end-to-end coreference resolution system can be based on a whole single model.
The aim of this stage is to study Sequence-to-Sequence [5] and Transformer [6] neural models for coreference resolution, integrating different types of attention mechanisms and possibly arbitrarily-long context [8], with the goal of understanding their impact in dealing with this complex NLP problem.

In this internship the student will implement parts of the systems for coreference resolution with Sequence-to-Sequence and Transformer neural models.
The student will run experiments on his own using GPUs, and the systems will be tested on the CoNLL Semeval 2012 benchmark [7].

Profile:
- Student for internship level stage (Master 2) in computer science, or from engineering school

- Computer science skills:
    Python programming with good knowledge of deep learning libraries (TensorFlow or PyTorch)
    Textual data manipulation (xml format, tabular format, CoNLL format)
- Interested in Natural Language Processing
- Skills in machine learning for probabilistic models

Context:

The internship may last from 4 up to 6 months, it will take place at LIG laboratory, GETALP team (http://lig-getalp.imag.fr/), starting from January/February 2019.
The student will be tutored by Marco Dinarelli (http://www.marcodinarelli.it) and Laurent Besacier (https://cv.archives-ouvertes.fr/laurent-besacier).
Interested candidates must send a CV and a motivation letter to marco.dinarelli@ens.fr and laurent.besacier@univ-grenoble-alpes.fr.

References:

[1]  Vincent Ng

    Supervised noun phrase coreference research: The first fifteen years.
    Proceedings of ACL, 2010

[2] Sam Wiseman, Alexander M. Rush, Stuart M. Shieber
    Learning Global Features for Coreference Resolution
    Proceedings of NAACL-HLT, 2016

[3] Kenton Leey, Luheng Hey, Mike Lewisz, and Luke Zettlemoyer
    End-to-end Neural Coreference Resolution
    Proceedings of EMNLP, 2017

[4] Kenton Lee Luheng He Luke Zettlemoyer
    Higher-order Coreference Resolution with Coarse-to-fine Inference
    Proceedings of NAACL, 2018

[5] Ilya Sutskever, Oriol Vinyals, Quoc V. Le
    Sequence to Sequence Learning with Neural Networks
    Proceedings of NIPS, 2014

[6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
    Attention Is All You Need
    Proceedings of NIPS, 2017

[7]  Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, Yuchen Zhang
     Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes
    Proceedings of EMNLP and  CoNLL-Shared Task, 2012

[8] Zhang, Jiacheng, et al. 'Improving the Transformer Translation Model with Document-Level Context.? EMNLP 2018.
 

------------------------
Laurent Besacier
Professeur à l'Univ. Grenoble Alpes (UGA)
Laboratoire d'Informatique de Grenoble (LIG)
Membre Junior de l'Institut Universitaire de France (IUF 2012-2017)
Responsable équipe GETALP du LIG
Directeur de l'école doctorale (ED) MSTII
-------------------------
!! Nouvelles coordonnées !!: LIG 
Laboratoire d'Informatique de Grenoble
Bâtiment IMAG
700 avenue Centrale
Domaine Universitaire - 38401 St Martin d'Hères
Pour tout contact concernant ED MSTII: passer par ed-mstii@univ-grenoble-alpes.fr
Nouveau tel: 0457421454
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6-42(2018-11-16) Post doc, U.R.I.-Octogone-Lordat, Universit de Toulouse, Toulouse, France



 Poste de post-doctorant à pouvoir au 14 janvier 2019 pour une durée de 6 mois (100%)
 La mission principale du postdoctorant sera d'établir une typologie des gestes dédiés à la correction phonétique. Il ou elle devra déterminer des invariants malgré une grande variabilité inter et intra enseignants.                       
 A l'issue du contrat, la personne recrutée devra livrer 10 gestes pertinents avec un descriptif à la fois acoustique et didactiques. Il devra ainsi fournir un dictionnaire des gestes correctifs avec des éléments de description a) des erreurs concernées par ces gestes (critères phonétiques), b) et de réalisation (critères de synchronisation avec la parole ; critères de réalisation gestuelle : éléments corporels impliqués : posture, visage, bras, mains, doigts + positions de départ et de fin du geste, dynamique)

Activités:
Il ou elle sera amené(e) à réaliser 2 tâches principales:                                                                                           
Tâche 1 : Etudier les paramètres caractéristiques des gestes et regrouper les gestes en famille
Tâche 2: Associer des gestes correctifs à un type d?erreur (segmental ou suprasegmental) ;
Pour ce faire, la personne recrutée sera amenée à annoter deux corpus existants [le corpus MULTIPHONIA (http://sldr.org/sldr000780/fr) et le corpus 'MVT', encore non référencé]. Il/elle devra identifier et décrire les paramètres pertinents liés la réalisation des gestes cibles en utilisant à la fois les critères de réalisation (éléments corporels concernés, positions départ et fin, dynamique) et les critères phonétiques (quel niveau/ erreur sont concernés par le geste).
L'enjeu sera d'annoter les paramètres gestuels du corpus de façon à servir de cahier des charges pour le développement d'un avatar dans la suite du projet.
Résultat attendu : listing des gestes relevés dans le corpus et proposition d'une typologie.

Compétences requises:
 -des connaissances approfondies en didactique des langues étrangères et en phonétique
- une maîtrise des techniques d'expérimentation et d'analyse, en particulier l'analyse de gestes
- une maîtrise des techniques de recueil et d'analyse des données
- une capacité à rendre compte et à présenter des résultats à l'oral
- de bonnes aptitudes rédactionnelles
- autonomie, sérieux et dynamisme

Environnement professionnel:
Le post-doc s'inscrit dans le cadre du projet INGPRO (INcidence des Gestes sur la PROnonciation) qui est un projet interdisciplinaire impliquant trois laboratoires toulousains (Octogone-Lordat, l?IRIT et le LAIRDIL) et l?entreprise Archean Technologies. Le/la post-doctorant(e)  sera encadré (e)  par Charlotte Alazard-Guiu, MCF au laboratoire Octogone-Lordat. Il / elle devra également travailler en collaboration avec les partenaires du projet et plus particulièrement en collaboration avec le stagiaire qui sera  recruté à l'IRIT en mars 2019 et qui travaillera sur la synchronisation geste/parole.

Charlotte Alazard-Guiu,
Maître de Conférences à l'Université de Toulouse II
DEFLE / UFR Langues (Bureau LA 393)
U.R.I Octogone-Lordat, EA4156 / Maison de la recherche (Bureau E. 1.15)
Mail: charlottealazard@gmail.comalazard@univ-tlse2.fr
Tel: (+33) 0561502001 (DEFLE)
      (+33) 0561502471 (Octogone-Lordat)
Page web: http://octogone.univ-tlse2.fr/accueil/octogone-lordat/membres/alazard-guiu-charlotte-398503.kjsp?RH=1295596505355

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6-43(2018-11-17 ) Language Resources Project Manager - Junior, at ELDA, Paris, France

The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Language Resources Project Manager ? Junior position. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field.

Language Resources Project Manager - Junior

Under the supervision of the Language Resources Manager, the Language Resources Project Manager ? Junior will be in charge of the identification of Language Resources (LRs), the negotiation of rights in relation with their distribution, as well as the data preparation, documentation and curation.

The position includes, but is not limited to, the responsibility of the following tasks:
?    Identification of LRs and Cataloguing
?    Negotiation of distribution rights, including interaction with LR providers, drafting of distribution agreements, definition of prices of language resources to be integrated in the ELRA catalogue or for research projects
?    LR Packaging within production projects
?    Data preparation, documentation and curation

Profile:
?    Master Degree or Equivalent in computational linguistics or similar fields
?    Experience in managing NLP tools
?    Good knowledge of script programming (Perl, Python or other languages)
?    Good knowledge of Linux
?    Dynamic and communicative, flexible to combine and work on different tasks
?    Ability to work independently and as part of a team
?    Proficiency in English, with strong writing and documentation skills. Communication skills required in a French-speaking working environment
?    Citizenship of (or residency papers) a European Union country

The position is to be filled as soon as possible and it is based in Paris.

Salary: between 25K? and 30K?

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org

ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

More opportunities at ELDA: http://www.elra.info/en/opportunities/

For further information about ELDA/ELRA, visit: http://www.elda.org

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6-44(2018-12-03) Post-doc at GIPSA-Lab, Grenoble, France

Job offer: Automatic assessment of the fluency of young readers (H/F)


We are offering a two-years Postdoc position at GIPSA-Lab, Grenoble, funded by the e-FRAN Fluence project, aiming at providing computer-assisted reading in the classroom. We will monitor the reading performance of 750 3th grade readers and 400 high school pupils with reading difficulties.
Postdoc Topic: Automatic text-to-speech alignment for assessing reading fluency
Expected qualifications: Applicants for the PostDoc position (3 years) must have a PhD in speech recognition with a strong background on Machine Learning and speech processing. Knowledge of computer-assisted language learning (CALL) applications and processing of children voices is a plus.
Applications: The candidates should apply though the CNRS portal: https://emploi.cnrs.fr/Offres/CDD/UMR5216-ALLBEL-004/Default.aspx?lang=EN. Feel free to contact : gerard.bailly@gipsa-lab.fr for additional information . Only shortlisted candidates will be contacted.
Expected starting date: The position is open and is to be filled ASAP.
Duration: 24 months
Gross salary: 2500?/month

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6-45(2018-12-03) Post-doc at KTH, Stockholm, Sweden
We are looking for a postdoc to conduct research in a multidisciplinary expedition project funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden?s largest individual research program, addressing compelling research topics that promise disruptive innovations in AI, autonomous systems and software for several years to come.
 
The project combines Formal Methods and Human-Robot Interaction with the goal of moving from conventional correct-by-design control with simple, static human models towards the synthesis of correct-by-design and socially acceptable controllers that consider complex human models based on empirical data. Two demonstrators, an autonomous driving scenario and a mobile robot navigation scenario in crowded social spaces, are planned to showcase the advances made in the project.
 
The focus of this position is on the development of data-driven models of human behavior that can be integrated with formal methods-based systems to better reflect real-world situations, as well as in the evaluation of the social acceptability of such systems. 
 
The candidate will work under the supervision of Assistant Prof. Iolanda Leite (https://iolandaleite.com/) and in close collaboration with another postdoctoral researcher working in the field of formal synthesis.
 
This is a two-year position. The starting date is open for discussion, but ideally, we would like the selected candidate to start in April 2019.
 
 
QUALIFICATIONS
 
Candidates should have completed, or be near completion of, a Doctoral degree with a strong international publication record in areas such as (but not limited to) human-robot interaction, social robotics, multimodal perception, and artificial intelligence. Familiarity with formal methods, game theory, and control theory is an advantage.
 
Documented written and spoken English and programming skills are required. Experience with experimental design and statistical analysis is an important asset. Applicants must be strongly motivated, be able to work independently and possess good levels of cooperative and communicative abilities.
 
We look for candidates who are excited about being a part of a multidisciplinary team.
 
 
HOW TO APPLY
 
The application should include:
 
1. Curriculum vitae.
2. Transcripts from University/ University College.
3. A brief description of the candidate's research interests, including previous research and future goals (max 2 pages).
4. Contact of two references. We will contact the references only for selected candidates.
 
The application documents should be uploaded using the KTH's recruitment system: https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:239307/where:4/
 
The application deadline is ** January 15, 2019 **   
 
 
ABOUT KTH
 
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe?s leading technical and engineering universities, as well as a key center of intellectual talent and innovation. We are Sweden?s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
 
-----------------
Iolanda Leite
Assistant Professor
KTH Royal Institute of Technology
School of Electrical Engineering and Computer Science
Department of Robotics, Perception and Learning (RPL)

Teknikringen 33, 4th floor, room 3424, SE-100 44 Stockholm, Sweden
Phone: +46-8 790 67 34
https://iolandaleite.com
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6-46(2018-12-04)PhD position at the University of Glasgow, Glasgow, UK

The University of Glasgow is still seeking applications for the following funded PhD position in the area of Explainable AI, Planning, and Human-Machine Interaction. Interested students should apply as soon as possible

 

Eligibility: UK/EU students only

Start date: 1 January 2019 (or as soon as possible thereafter)

Full details at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=98605  

 

Many future industrial operations will be carried out by teams consisting of humans and machines. In this project, the student will investigate how human-machine trust and explainable/transparent artificial intelligence affect such human-machine collaborative tasks. The work will concentrate on the communication aspects: how the machine communicates its intentions and reasoning processes to the human, and how the human can query and interact with the robot’s plan.

 

The project will be driven by oilfield drilling applications, which involve control of complex equipment in a dynamic environment, with an increasing level of automation. In this setting, close coordination and trust between the human crew and the automation system is required: the crew must both understand why the machine acts the way it does, as well as be confident it has taken all available information into account.

 

The student should have excellent experience, enthusiasm and skills in the areas of artificial intelligence and/or automated planning and reasoning and/or natural language or multimodal interaction. Applicants must hold a good Bachelor’s or Master’s degree in a relevant discipline.

 

The project is an EPSRC iCASE award with Schlumberger Gould Research and it is expected that the student will spend some time working with the company in Cambridge. This will give you a great opportunity of working in an internationally excellent research group as well as a leading player in the oil and gas industry. Travel and accommodation costs will be paid during the time at Schlumberger.

 

For more information, please contact Dr Mary Ellen Foster MaryEllen.Foster@glasgow.ac.uk. Feel free to contact me directly if you have questions, but only applications received through the University of Glasgow (as linked from the FindAPHD page) can be considered officially.

 

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6-47(2018-12-05 )Speech scientist at ReadSpeaker, Huis ter Heide, the Netherlands
Speech scientist at ReadSpeaker, Huis ter Heide, the Netherlands
 
ReadSpeaker is looking for a full-time speech scientist to work on neural text-to-speech synthesis for a large variety of languages. The position to be filled is located at the office in Huis ter Heide, the Netherlands. It is very centrally located, close to the city of Utrecht. You will be part of an international team of speech- and DNN/ML-scientists working in the Netherlands, Sweden, the US, South Korea, and Japan.

Requirements

? Have a MSc or PhD in Computer Science, Electrical Engineering, or related discipline with specialization in speech synthesis, recognition, natural language processing, or machine learning.
? Solid Machine Learning background and familiar with standard speech and machine learning techniques (DNN, CNN, RNN, LSTM, Deep learning).
? Experience in building speech, natural language processing and/or machine learning systems
? Good skills in programming languages C/C++ and/or Python, and algorithm implementation
? Moderate experience in Software development
? Good written and spoken English communication skills
 
 
Contact information:
 
ReadSpeaker
Dolderseweg 2A
3712BP Huis ter Heide
+31-(0)30-6924490
The Netherlands
Esther Klabbers
 
 
 
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6-48(2018-12-05) Post-doc at GIPSA, Grenoble, France

Le GIPSA cherche un(e) jeune docteur(e), spécialiste de reco automatique pour le projet e-FRAN Fluence pour l'évaluation de la fluence de jeunes lecteurs.
  Merci de diffuser cette offre de PostDoc de 12 mois, renouvelable une fois (https://emploi.cnrs.fr/Offres/CDD/UMR5216-ALLBEL-004/Default.aspx) aux post-docs de vos labos potentiellement concerné(e)s/intéressé(s)s

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6-49(2018-12-06) Positions at Qwant, Paris, France

La section recherche de Qwant propose un stage et un poste permanent en Traitement Automatique des Langues dans le cadre applicatif de la Recherche d'Information, lié à des problématiques d'analyse de sentiments/d'opinion, traduction automatique, l'extraction d'information sémantique, agents conversationnels, etc. Ces postes sont liés à nos projets de recherche avec l'INRIA & européens H2020 dans lesquels ces problématiques seront explorées.

Les postes à pourvoir seront situés à Paris, dans nos locaux près de la Porte Dauphine au 7 rue Spontini. L'équipe TAL est au cœur d'un écosystème dynamique de recherche applicatif composé, entre autres, des équipes de Traitement de l'Image, Apprentissage Automatique pour la Recherche d'Information, Protection de la vie Privée, et Cartographie. Les équipements comprennent notamment un cluster de calcul GPU dernier cri.

Pour toute information complémentaire, vous pouvez me contacter directement à cette adresse : c.servan_CHEZ_qwant.com.

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6-50(2018-12-10) PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.

 

Subject: PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.

Job description:

 

 

The Medical University of Vienna (MUV), Austria, seeks to fill a position of a PhD-student within the project ?Objective differentiation of dysphonic voice quality types?. The candidate must hold a master?s degree, preferably in (one of) the fields of sound engineering, acoustical engineering, audio signal processing, or similar. The work will be conducted at the Division of Phoniatrics-Logopedics within the Department of Otorhinolaryngology of the MUV.

The workgroup hosting the project is interested in the assessment of voice parameters relevant to the medical diagnosis and clinical care of voice disorders. A focus is given to functional assessment of voice, especially to the objective description of voice quality. The levels of description include kinematics of voice production, voice acoustics, and auditory perception of voice. Clinical studies are conducted with a laryngeal high-speed camera that records vocal fold vibration at 4000 frames per second. Microphone signals of the voice are recorded in parallel. Vibratory patterns of the vocal folds are analysed visually and computationally via modelling. Trajectories of vocal fold edges, spatial arrangements thereof, and glottal area waveforms are analysed. Regarding acoustics, analysis of audio recordings involves the implementation, testing, and training of specialized synthesizers for pathological voices. On the level of auditory perception, listening experiments are conducted, especially experiments involving discrimination tasks.

Mandatory skills of the candidate are MATLAB programming, speech signal processing, psychoacoustics, good knowledge of English, good communication skills, and excellent analytical thinking. Optional skills of the candidate are knowledge of German, experience in a health care profession, image and video processing, Python, PureData, object-oriented programming, software engineering, version control (Subversion, Git, or similar), SQL, and XML.

The project duration is 4-5 years. The Austrian Science Fund (FWF) budgets for doctoral candidates a gross salary of 2.112,40 Euro per month. Application documents can be submitted to philipp.aichinger@meduniwien.ac.at by October 31st, 2018. Interviews are planned for November 2018.The project is planned to start in December 2018.

Information regarding the beautiful city of Vienna can be found at https://www.meduniwien.ac.at/web/en/international-affairs/living-in-vienna/.

--
MedUni Wien Signatur EN

Univ.-Ass. DI Dr.techn. Philipp Aichinger
Research scientist

Medical University of Vienna
Division of Phoniatrics-Logopedics
Department of Otorhinolaryngology

Währinger Gürtel 18-20, 1090 Vienna, Austria
T: +43 (0)1 40400-11670
M: +43 (0)699 12 29 28 69
philipp.aichinger@meduniwien.ac.at
www.meduniwien.ac.at

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6-51(2018-12-12) Research and Development Engineer at ELDA Paris

The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Research and Development Engineer.

Under the supervision of the CEO, the responsibilities of the R&D Engineer include designing, developing, documenting, deploying and maintaining tools, software components or applications for Language Resource production and management.
He/she will be in charge of managing the current Language Resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded research and development projects.

This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.

Profile:

  •     PhD in Computer Science, Natural Language Processing, or equivalent
  •     Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.)
  •     Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)
  •     Good level in Python
  •     Knowledge of a distributed version control system (Git, Mercurial)
  •     Knowledge of SQL and of RDBMS (PostgreSQL preferred)
  •     Knowledge of XML and of standard APIs (DOM, SAX)
  •     Familiarity with open source and free software
  •     Knowledge of a statically typed functional programming language (OCaml preferred) is a plus
  •     Good level in French and English, with strong writing and documentation skills in both languages
  •     Dynamic and communicative, flexible to work on different tasks in parallel
  •     Ability to work independently and as part of a multidisciplinary team
  •     Citizenship (or residency papers) of a European Union country


Permanent position. Applications will be considered until the position is filled.

Salary is commensurate with qualifications and experience.
Benefits: complementary medical insurance; meal vouchers.

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to:

ELDA
9, rue des Cordelières
75013 Paris
FRANCE
Fax : 01 43 13 33 30
Mail job@elda.org

ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA and ELRA, visit:
http://www.elra.info

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6-52(2018-12-12) Master R2 Internship, Loria, Nancy, France

Master R2 Internship in Natural Language Processing: Online hate

speech against migrants

 

 

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

 

Team: Multispeech, LORIA-INRIA

 

Contact: illina@loria.fr, dominique.fohr@loria.fr

 

Duration: 5-6 months

 

Deadline to apply : January 10th, 2018

 

Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

 

Motivations and context

According to the 2017 International Migration Report, the number of international migrants worldwide has grown rapidly in recent years, reaching 258 million in 2017, among whom 78 million in Europe. A key reason for the difficulty of EU leaders to take a decisive and coherent approach to the refugee crisis has been the high level of public anxiety about immigration and asylum across Europe. There are at least three social factors underlying this attitude (Berri et al, 2015): the increase in the number and visibility of migrants; the economic crisis that has fed feelings of insecurity; the role of mass media. The last factor has a major influence on the political attitudes of the general public and the elite. Refugees and migrants tend to be framed negatively as a problem. This translates into a significant increase of hate speech towards migrants and minorities. The Internet seems to be a fertile ground for hate speech (Knobel, 2012).

The goal of this master internship is to develop a methodology to automatically detect hate speech in social network data (Twitter, YouTube, Facebook).

In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. Developments in Neural Network (Mikolov et al., 2013a) led to a renewed interest in the field of distributional semantics, more specifically in learning word embeddings (representation of words in a continuous space). Computational efficiency was one big factor which popularized word embeddings. The word embeddings capture syntactic as well as semantic properties of the words (Mikolov et al., 2013b). As a result, they outperformed several other word vector representations on different tasks (Baroni et al., 2014).

Our methodology in the hate speech classification will be related on the recent approaches for text classification with Neural Networks and word embeddings. In this context, fully connected feed forward networks (Iyyer et al., 2015; Nam et al., 2014), Convolutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015) and also Recurrent/Recursive Neural Networks (RNN) (Dong et al., 2014) have been applied. On the one hand, the approaches based on CNN and RNN capture rich compositional information, and have outperformed the state-of-the-art results in text classification; on the other hand they are computationally intensive and require careful hyperparameter selection and/or regularization (Dai and Le, 2015).

 

Objectives

 

The goal of this Master internspeech Develop a new methodology to automatically detect hate speech, based on machine learning and Neural Networks. Human detection of this material is infeasible since the contents to be analyzed are huge. In recent years, research has been conducted to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods (Schmidt et al. 2017). Although current methods have reported promising results, their evaluations are largely biased towards detecting content that is non-hate, as opposed to detecting and classifying real hateful content (Zhang et al., 2018). Current machine learning methods use only certain task-specific features to model hate speech. We propose to develop an innovative approach to combine these pieces of information into a multi-feature approach so that the weaknesses of the individual features are compensated by the strengths of other features (explicit hate speech, implicit hate speech, contextual conditions affecting the prevalence of hate speech, etc.).

 

References

 

Baroni, M., Dinu, G., and Kruszewski, G. (2014). ?Don?t count, predict! a systematic comparison of context-counting vs. contextpredicting semantic vectors?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 238-247.

Berri M, Garcia-Blanco I, Moore K (2015), Press coverage of the Refugee and Migrant Crisis in the EU: A Content Analysis of five European Countries, Report prepared for the United Nations High Commission for Refugees, Cardiff School of Journalism, Media and Cultural Studies.

Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc

Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54.

Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691.

Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112.

Kim, Y. (2014). ?Convolutional neural networks for sentence classification?. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751.

Knobel M. (2012). L?Internet de la haine. Racistes, antisémites, néonazis, intégristes, islamistes, terroristes et homophobes à l?assaut du web. Paris: Berg International

Mikolov, T., Yih, W.-t., and Zweig, G. (2013a). ?Linguistic regularities in continuous space word representations?. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746-751.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc.

Nam, J., Kim, J., Loza Menc__a, E., Gurevych, I., and Furnkranz, J. (2014). ?Large-scale multi-label text classification ? revisiting neural networks?. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, volume 8725, pages 437-452.

Schmidt A., Wiegand M.(2017). A Survey on Hate Speech Detection using Natural Language Processing, Workshop on Natural Language Processing for Social Media

Zhang, Z., Luo, L (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662

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6-53(2018-12-12) Research engineer or post-doc position, Loria, Nancy, France

Research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

 

Team: Multispeech, LORIA-INRIA

 

Contact: illina@loria.fr, dominique.fohr@loria.fr

 

Duration: 12-15 months

 

Deadline to apply : January 20th, 2019

 

Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

 

Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech.

The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN.

The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.

 

Main activities

  • study and implementation of a noisy speech enhancement module and a propagation of uncertainty module;
  • design a semantic analysis module;
  • design a module taking into account the semantic and uncertainty information.

Skills

Strong background in mathematics, machine learning (DNN), statistics

Following profiles are welcome, either:

  • Strong background in signal processing

or

  • Strong experience with natural language processing

Excellent English writing and speaking skills are required in any case.

 

References

 

[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018.

[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017.

[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016.

[Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.

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6-54(2018-12-14) Master R2 internship at Loria, Nancy, France

Master R2 internship in Natural Language Processing: Introduction of semantic

information in a speech recognition system

 

 

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

 

Team: Multispeech, LORIA-INRIA

 

Contact: illina@loria.fr, dominique.fohr@loria.fr

 

Duration: 5-6 months

 

Deadline to apply : January 20th, 2019

 

Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

 

Motivations and context

 

Semantic and thematic spaces are vector spaces used for the representation of words, sentences or textual documents. The corresponding models and methods have a long history in the field of computational linguistics and natural language processing. Almost all models rely on the hypothesis of statistical semantics which states that: statistical schemes of appearance of words (context of a word) can be used to describe the underlying semantics. The most used method to learn these representations is to predict a word using the context in which this word appears [Mikolov et al., 2013b, Pennington et al., 2014], and this can be realized with neural networks. These representations have proved their effectiveness for a range of natural language processing tasks [Baroni et al., 2014]. In particular, Mikolov?s Skip-gram and CBOW models et al. [Mikolov et al., 2013b, Mikolov et al., 2013a] have become very popular because of their ability to process large amounts of unstructured text data with reduced computing costs. The efficiency and the semantic properties of these representations motivate us to explore these semantic representations for our speech recognition system.

Robust automatic speech recognition (ASR) is always a very ambitious goal. Despite constant efforts and some dramatic advances, the ability of a machine to recognize the speech is still far from equaling that of the human being. Current ASR systems see their performance significantly decrease when the conditions under which they were trained and those in which which they are used differ. The causes of variability may be related to the acoustic environment, sound capture equipment, microphone change, etc.

Objectives

The speech recognition (ASR) stage will be supplemented by a semantic analysis to detect the words of the processed sentence that could have been misrecognized and to find words having similar pronunciation and matching better the context. For example, the sentence « Silvio Berlusconi, prince de  Milan » can be recognized by the speech recognition system as : « Silvio Berlusconi, prince de mille ans ». Good semantic context representation of the sentence could help to find and correct this error.

The Master internship will be devoted to the innovative study of the taking into account of semantics through predictive representations that capture the semantic features of words and their context. Research will be conducted on the combination of semantic information with information from denoising to improve speech recognition. As deep neural networks (DNNs) can model complex functions and get outstanding performance, they will be used in all our modeling.

References

[Deng, 2014] Deng, L. Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3-4), 197?387, 2014.

[Goodfellow et al., 2016] Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press. http://www.deeplearningbook.org, 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013.

[Mikolov et al., 2013b] Mikolov, T., Sutskever, I., Chen, T. Corrado, G.S.,and Dean, J. Distributed representations of words and phrases and their compositionality, in Advances in Neural Information Processing Systems, 2013, pp. 3111?3119.

[Pennington et al., 2014] Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543.

[Povey et al, 2011] Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motl?cek, P., Qian, Y., Schwarz, Y., Silovsky, J., Stemmer, G., Vesely, K. The Kaldi Speech Recognition Toolkit, Proc. ASRU, 2011.

[Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016.

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6-55(2018-12-18) Postdoc at IRIT, Toulouse, France

L'équipe SAMoVA de l'IRIT (Institut de Recherche en Informatique de Toulouse) recrute un chercheur ou une chercheuse en post-doctorat pour le projet collaboratif LinTo (PIA - Programme d?Investissements d'Avenir), projet d?assistant conversationnel destiné à opérer en contexte professionnel pour proposer des services en lien avec le déroulement de réunions.  

 

Ce travail post-doctoral concerne l?analyse du flux audio pour extraire un ensemble d?indicateurs non verbaux destinés à compléter la transcription automatique générée par d?autres partenaires du projet. Cet enrichissement aura pour rôle d?apporter des indications précieuses pour aider à la compréhension du déroulement des réunions, que ce soit au niveau des interactions, entre participants ou avec l?assistant vocal, ou de manière plus détaillée au niveau du contenu des échanges.

Plusieurs pistes de recherche pourront être explorées en fonction du profil de la personne recrutée ainsi des situations étudiées dans le cadre du projet :
- Analyse acoustique pour le recherche de marqueurs prosodique pertinents ;
- Exploration des approches de type Speech2Vect pour extraire des indicateurs plus marqués sémantiquement ;
- Application de méthodes d'apprentissage semi-supervisé dans un contexte faiblement annoté.

 

 

Informations Pratiques :
Poste à pourvoir : post-doc
Durée: 12-20 mois à partir de février 2019
Domaine : analyse acoustique - traitement automatique de la parole -  apprentissage automatique - interaction conversationnelle
Lieu : Institut de Recherche en Informatique de Toulouse (Université Paul Sabatier) -  Equipe SAMoVA
Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr
Dossier de candidature : à envoyer avant le 15 janvier 2019.
Détail de l'offre :  https://www.irit.fr/recherches/SAMOVA/pagejobs.html
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6-56(2018-12-18) Post Doctoral Research Associate, University of ESSEX, UK
We are looking for a Post Doctoral Research Associate to collaborate at the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction taking into account the role of human chemosignals perception. This will involve also the development of robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, as well as the design and implementation of software for the execution of experiments with adaptive VR stimulation.



Application closing date 02/01/2019

 

Application Links

 

 

 
Job reference REQ02121

 
Job Details

 The School of Computer Science and Electronic Engineering, the Department of Psychology, and the Essex Brain-Computer Interfaces and Neural Engineering Lab are pleased to announce this postdoctoral position in the Horizon 2020 project 'POTION: Promoting social interaction through emotional body odours?. The project will last five years and start in January 2019 and includes partners from the Universities of Pisa (Italy), Padova (Italy), and Essex (UK), the Universitat Politecnica De Valencia (Spain), the Katholieke Universiteit Leuven (Belgium), and the Karolinska Institutet (Sweden), and three companies ISPA CRL (Portougal), SRA Instruments (France) and Feel-Ing s.r.l. (Italy). POTION proposes a novel technological paradigm to delve deeper into understanding meaningful social interaction, combining new knowledge about the chemical composition of human social chemosignals together with a novel olfactory-based technology designed to drive social behaviour.
 
 
Duties of the Role
The Essex team's work on the project focuses on the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction. This models will be applied to evaluate socially relevant variables, such as trust, presence and inclusion as well as to generate optimal stimuli in artificially mediated social interactions. In particular, the models will cover the role of human chemosignal perception in social interactions. The models will be identified and tested using neurophysiological data (e.g. EEG), peripheral physiological activation (i.e., ECG, RESP, EDA) and behavioural changes (i.e., f-EMG) collected using VR scenarios of increasing complexity.
 
The successful applicant will research and develop Bayesian (DCM and Active Inference) computational models of multimodal social interaction with an emphasis on the role of human chemosignals. They will also develop robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, design and implement software for the execution of experiments with VR stimulation, and contribute to the reporting and dissemination of the project.
 
 
Skills and qualifications required
 
Applicants are expected to hold a PhD (or be close to completion) in Computational Neuroscience, Brain-computer Interfaces, Neural Engineering, Psychology, Machine Learning, Statistics, Physics, Mathematics, Computer Science or a closely related discipline, or equivalent professional experience or practice. The ideal candidate will have significant experience in computational modelling of social interaction, signal processing, statistical modelling of neural signals and processes, brain-computer interfaces, and virtual reality interfaces. Applicants are also expected to have a strong publication record (relative to their career stage) as first author, ideally including publications in 1st quartile journals in relevant areas.
 
We strongly encourage women to apply as they are currently under-represented in the School of Computer Science and Electronic Engineering. 

At the University of Essex internationalism is central to who we are and what we do. We are committed to being a cosmopolitan, internationally-oriented university that is welcoming to staff and students from all countries and a university where you can find the world in one place.


Who we are


University of Essex has just been awarded the prestigious title of  'University of the Year' by the Times Higher Education for 'transforming the lives of a growing student body? and ' putting  both staff and students first'

The School of Computer Science and Electronic Engineering (CSEE) at the University of Essex has an outstanding reputation for teaching and high-quality research in artificial intelligence, biomedical engineering, brain-computer interfaces, computer games, evolutionary computation, human language technology, robotics, networks and optoelectronics. Particularly relevant to this application is our research in artificial intelligence and in life and medical sciences applications, which was judged as world-leading in the recent Research Excellence Framework, the system for assessing the quality of research in UK higher education institutions. An important centre spanning both areas is the Essex Brain-Computer Interface and Neural Engineering (BCI-NE) Laboratory. The BCI lab was founded in 2004 by Dr Citi among others, and is one of the largest and best equipped in Europe. For over a decade, it has produced highly visible internationally leading research, with international collaborators at MIT, Berkley, the European Space Agency, and many others. Our members have led high-profile externally funded projects in the area of Assisted Living Technologies. Since 2007 our research has featured prominently in the UK?s Department of Health?s annual report on research and development work relating to assistive technology, e.g. see the 2012 report, which was presented to parliament.
 
The successful applicant will work in collaboration with the Department of Psychology at the University of Essex. This department was ranked 13th out of more than 100 in the UK in the most recent Research Excellence Framework (REF 2014), with 90% of our research rated as world-leading or internationally excellent. Academic staff in the department have wide-ranging and world-leading expertise in vision, cognition, cognitive neuroscience, health and social psychology. Students and staff make use of facilities in the Centre for Brain Science, a purpose-built facility dedicated to research and with considerable resources for understanding brain and behaviour.

The research facilities are located on our Colchester Campus, which  is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester ? England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour. Home to over 13,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place. Colchester has a relatively low cost of living, while being well connected to London, the coast, and areas of natural beauty in East Anglia.
 
Please see the link below for a full job description and person specification which outlines the full duties, skills, qualifications and experience needed for this role plus more information relating to the post. We recommend you read this information carefully before making an application.  Applications should be made on-line, but if you would like advice or help in making an application, or need information in a different format, please telephone the Resourcing Team (+44 1206 876559). 
 
Feel free to  contact us (Dr Citi, (CSEE) lciti@essex.ac.uk (PI), Dr Ognibene (CSEE) dimitri.ognibene@essex.ac.uk, Dr Foulsham (PSYCH)  foulsham@essex.ac.uk) for an informal discussion about this post.
 
Job reference REQ02121

Application closing date02/01/2019

Application Links

https://vacancies.essex.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=271640IULU&WVID=9918109NEm&LANG=USA

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6-57(2018-12-20) Post-doc at Loria, Nancy

L'équipe Multispeech recrute un post-doctorant sur l'articulation de l'allemand par des français


Etude articulatoire des sons de l?allemand prononcés par des français

Contexte

L?équipe MULTISPEECH, de Inria Nancy-Grand Est, étudie différents aspects de la modélisation de la parole, comme la production, la reconnaissance et la synthèse de la parole. Dans le cadre du projet collaboratif e-FRAN METAL qui porte sur l?utilisation du numérique dans l?éducation, des techniques de la parole sont adaptées, enrichies, et mises en ?uvre pour aider à l?apprentissage d?une langue étrangère. L?objectif consiste alors à détecter les défauts de prononciation des apprenants (prononciation des sons et intonation), et à proposer des diagnostics pour aider l?apprenant à améliorer sa prononciation.

Missions

Dans ce cadre, une étude articulatoire portera sur la prononciation des sons de l?allemand par des locuteurs français. Plus précisément, une partie de l?étude concernera les consonnes fricatives qui ne sont pas présentes dans le système français, notamment les fricatives palatale (le [ç] de « ich ») et vélaire (le [x] de « Buch »). Afin d?analyser les interférences entre la langue première et la langue seconde sur le plan articulatoire,  la prononciation des consonnes proches de celles étudiées, que ce soit dans le système natif des apprenants (le français) ou le système non natif (l?allemand) fera également partie de l?étude.

Le travail commencera par une collecte de données articulatoires. Pour ce faire, le chercheur utilisera un articulographe qui fait partie d?une plateforme d?acquisition multimodale disponible au laboratoire d?accueil. L?analyse des sorties de l?articulographe se fera grâce au logiciel VisArtico qui a été développé au sein de l?équipe. Les données acoustiques, synchronisées avec les données articulatoires, seront recueillies et analysées. Enfin, une étude perceptive restreinte sera menée afin mettre en correspondance articulation et perception.

L?étude doit permettre une meilleure compréhension des difficultés articulatoires rencontrées par les français parlant allemand en fonction des interférences entre les deux langues. Elle doit également permettre de présenter des stratégies pour prendre en compte ces difficultés afin d?améliorer  la qualité des retours faits aux apprenants dans le domaine de l?apprentissage de l?oral d?une langue étrangère. Les résultats seront appliqués directement dans le projet e-FRAN METAL.

 

Prérequis : le candidat devra avoir soutenu sa thèse entre 2015 et 2018.

Durée du poste à pourvoir : 18 mois,

Début : dès que possible et au plus tard fin mars 2019 

Lieu : INRIA Grand-Est, Nancy, France.

Encadrants : Anne Bonneau (anne.bonneau@loria.fr) et Slim Ouni (slim.ouni@loria.fr)

Profil et compétences recherchées :

-        Doctorat portant sur la phonétique ou le traitement de la parole (thèse soutenue thèse entre 2015 et 2018)

-        Bonnes connaissances en phonétique

-        Des connaissances de base en programmation (pouvoir écrire des scripts simples)

-        des bases en langues française et allemande.

 

 

Références bibliographiques.

D Jouvet, A. Bonneau, J. Trouvain, F. Zimmerer, Y. Laprie, B. Moebius. 'Analysis of phone confusion matrices in a manually annotated French-German learner corpus?. Workshop on Speech and Language Technology in Education, Sep 2015, Leipzig, Germany. Proceedings SLaTE 2015, Workshop on Speech and Language Technology in Education. <hal-01184186>

S. Ouni, L. Mangeonjean, I. Steiner. VisArtico: a visualization tool for articulatory data. 13th Annual Conference of the International Speech Communication Association ? InterSpeech 2012, Sep 2012, Portland, OR, United States. 2012.

Wieling, M., Veenstra, P., Adank, P., and Tiede, M.  (2015), 'Comparing L1 and L2 speakers using articulography' Proceedings of the International Congress of Phonetic Sciences (ICPhS).Glasgow.  

 

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6-58(2018-12-21) Stage de fin d’études d’Ingénieur ou de Master 2, INA, Paris, France

Étude et développement d’une solution de synthèse vocale pour l’imitation d’un locuteur
 
Stage de fin d’études d’Ingénieur ou de Master 2 – Année académique 2018-2019
 
 
Mots clés : Synthèse vocale (Text-to-speech – TTS), Adaptation de locuteur, Deep Neural Network (DNN), Machine Learning
 
Contexte
 
L’Institut national de l’audiovisuel (Ina) est un établissement public à caractère industriel et commercial (EPIC) dont la mission principale consiste à archiver et valoriser la mémoire audiovisuelle française (radio, télévision et web média). À ce jour, plus de 17 millions d’heures de documents télé et radio ont été conservées.
 
Ce stage s’inscrit dans le cadre du projet Saphir de restauration d’anciens disques gravés. Un certain nombre de ces disques sont fracturés et certaines portions des disques sont manquantes. La finalité du projet global consiste à utiliser des technologies de synthèse vocale pour combler les parties du signal pour lesquelles le support est manquant, ou trop endommagé pour pouvoir être décodé.
 
Objectifs du stage
 
Le but du projet est de proposer et d’implémenter une solution de synthèse paramétrique par réseaux de neurones profonds (DNN) pour la création d’un modèle de voix universel en français à partir d’un large corpus et pour l’adaptation de ce modèle vers une locuteur particulier à partir d’un corpus de taille réduite (entre 1 et 5 minutes de parole). Il sera aussi nécessaire d’évaluer l’influence de la taille du corpus utilisé pour l’adaptation sur la qualité de la synthèse résultante. 
 
Le stage sera organisé en plusieurs étapes :
 
• Réaliser un état de l’art de la synthèse par modèles statistiques (HMM, DNN) • Réaliser un état de l’art de l’adaptation des systèmes de synthèse à un locuteur particulier • Faire l’inventaire des corpus (parole + transcription) de l’Ina (ou extérieurs en accès libre) qui pourraient servir à l’entrainement des modèles de synthèses • Entrainer un modèle universel (Universal background model – UBM) à partir d’un large corpus (2h - 50h) de plusieurs locuteurs • Adapter le modèle vers un locuteur cible à partir d’un corpus de taille réduite (1min – 5min) • Évaluer l’influence de la taille du corpus d’adaptation pour la qualité de la synthèse résultante
 
Le langage de programmation utilisé dans le cadre de ce stage sera Python. Le stagiaire aura accès aux ressources de calcul de l’Ina (serveurs et clusters), ainsi que d’un desktop performant avec 2 GPU de génération récente.
 
Possibilité de poursuivre en thèse CIFRE selon les résultats du stage et les offres disponibles.
 
Valorisation du stage
 
Différentes stratégies de valorisation des travaux du stagiaire seront envisagées, en fonction du degré de maturité des travaux réalisés : • Diffusion des outils d’analyse réalisés sous licence open-source via le dépôt GitHub de l’Ina : https://github.com/ina-foss • Rédaction de publications scientifiques.
 
Conditions du stage
 
Le stage se déroulera sur une période de 4 à 6 mois, au sein du service de la Recherche de l’Ina. Il aura lieu sur le site Bry2, situé au 18 Avenue des frères Lumière, 94366 Bry-sur-Marne. Le stagiaire sera encadré par Marc Evrard (mevrard@ina.fr). Gratification : environs 550 Euros par mois.
 
Profil recherché
 
Bac +5 dans le domaine de l’informatique et de l'IA. Compétence en langage Python et expérience dans l’utilisation de bibliothèques de machine learning et big data. Capacité à réaliser une étude bibliographique à partir d’articles scientifiques rédigés en anglais.
 
Bibliographie
 
Chenot, J.-H., Laborelli, L., Noiré, J.-E. (2018). Saphir: Optical Playback of Damaged and Delaminated Analogue Audio Disc Records,  ACM Journal on Computing and Cultural Heritage (JOCCH) vol.11, no. 3, August 2018. <https://hal.archives-ouvertes.fr/hal-01885324>.
 
Ze, H., Senior, A., & Schuster, M. (2013). Statistical parametric speech synthesis using deep neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 7962–7966). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/ICASSP.2013.6639215
 
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Saurous, R. A. (2017). Tacotron: Towards End-to-End Speech Synthesis. In Interspeech.
 
Wu, Z., Swietojanski, P., Veaux, C., Renals, S., & King, S. (2015). A Study of Speaker Adaptation for DNN-Based Speech Synthesis, 5. In Interspeech.

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6-59(2018-12-22) Postdoctoral researcher and doctoral student positions,IDIAP, Martigny, Switzerland

Postdoctoral researcher and doctoral student positions in ubiquitous computing and
computer vision at Idiap, Switzerland


The Social Computing Group at Idiap is seeking creative and motivated young researchers
(postdocs and doctoral students) for two topics:

Topic 1: This is an opening for ubiquitous computing researchers (postdoc or doctoral
student) to work on machine learning applied to smartphone sensor data for large-scale
behavioral analysis in daily life, with experience in machine learning for activity
recognition, deep learning, and mobile data science, in the context of a large European
project.

Topic 2: This is an opening for a computer vision or multimodal interaction postdoc
researcher to work on social interaction analysis, with experience in deep learning
applied to face, body, and gesture analysis, in the context of an innovation project with
an industrial partner.

The positions offer the opportunity to do exciting work on human-centered analysis of
everyday life behavior. The researchers will collaborate with Prof. Daniel Gatica-Perez
and his research group. The candidates will have degrees in computer science or
engineering, with experience in machine learning, data science, and ubicomp (for topic
1); and computer vision and machine learning for multimodal interaction (topic 2).
Postdoctoral researchers are expected to have a strong publication record. Candidates to
the PhD student position will become doctoral students at EPFL contingent upon acceptance
by the EPFL Doctoral School. The applicants will have strong programming skills (Python,
C/C++ in the Linux environment.) Experience with deep learning (TensorFlow, pyTorch) is a
definite asset.

Salaries are competitive and starting date is immediate. Interviews will start upon
reception of applications until the positions are filled.

Interested candidates are invited to submit a cover letter, a detailed CV, and the names
of three references through the Idiap online recruitment system:
http://www.idiap.ch/en/allnews/postdoctoral-researcher-doctoral-student-positions-in-ubiquitous-computing-and-computer-vision. Interested candidates can also contact Prof. Daniel Gatica-Perez
(gatica@idiap.ch).

About Idiap

Idiap is an independent, not-for-profit, research institute funded by the Swiss Federal
Government and the State of Valais. The institute is located in Martigny in Valais, a
scenic region in the south of Switzerland, surrounded by the highest mountains of Europe,
which offers exceptional quality of life, exciting recreational activities as well as
varied cultural activities, and within close proximity to Lausanne and Geneva.

Idiap is an equal opportunity employer, and offers competitive salaries and conditions in
a young, dynamic, and multicultural environment. English is the working language.

For frequently asked questions (FAQs) about working and living in Switzerland, please go
to http://www.idiap.ch/en/faq

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6-60(2019-01-05) Two postdoctoral researcher project /researcher positions in speech processing, University of Eastern Finland, Finland
Two Postdoctoral Researcher/Project Researcher positions in speech processing

The University of Eastern Finland, UEF, is one of the largest multidisciplinary universities in Finland. We offer education in nearly one hundred major subjects, and are home to approximately 15,500 students and 2,500 members of staff. We operate in Joensuu and Kuopio. In international rankings, we are ranked among the leading universities in the world.

The Faculty of Science and Forestry operates on the Kuopio and Joensuu campuses of the University of Eastern Finland. The mission of the faculty is to carry out internationally recognised scientific research and to offer research-education in the fields of natural sciences and forest sciences. The faculty invests in all of the strategic research areas of the university. The faculty’s environments for research and learning are international, modern and multidisciplinary.  The faculty has approximately 3,800 Bachelor’s and Master’s degree students and some 490 postgraduate students. The number of staff amounts to 560. http://www.uef.fi/en/lumet/etusivu

We are now inviting applications for 

two Postdoctoral Researcher/Project Researcher positions in speech processing funded by the Academy of Finland at the School of Computing, Joensuu Campus.

  • One position in machine learning for speaker modelling (e.g. speaker verification, voice anti-spoofing, voice conversion, text-to-speech, or similar)
  • One position in perceptual and/or acoustic speaker characterization (e.g. phonetics/linguistics, speech modelling, statistical methods)

Both positions are filled in the Academy of Finland funded NOTCH research project (NOn-cooperaTive speaker CHaracterization), led by Associate Professor Tomi H. Kinnunen. The project aims at advancing the state-of-the-art in automatic speaker verification (defence) and voice conversion (attack) under a generic umbrella of non-cooperative speech, whether being induced by spoofing attacks, disguise, or other less expected intentional voice modifications. The NOTCH project applies multi-disciplinary research methods. The ideal candidate for the first position will have a background in machine learning or signal processing for speaker modelling and characterization. You may have a background in recognition, conversion or synthesis methods, as long as you are seasoned in state-of-the-art machine learning theory and practice (especially deep learning). The ideal candidate for the second position will have a background in acoustic-phonetic or perceptual methods for speaker characterization and will be fluent in devising novel statistical analysis methods such as linear mixed effect models. For both positions, multi-disciplinary thinking and willingness to contribute to both themes is considered a plus.

The Computational Speech Group of the School of Computing (https://www.uef.fi/web/speech/) , formed officially in 2018, works in the facilities of Joensuu Science Park, provides access to a modern research infrastructure and is a strongly international working environment. We are a group of dedicated individuals who do not want to follow a linear research path – we keep our mind open to high-risk new directions and collaborations. We hosted the Odyssey 2014 conference, were a partner in the H2020-funded OCTAVE project, and are known as a co-founder of the Automatic Speaker Verification and Countermeasures (ASVspoof) challenge series (http://www.asvspoof.org/). Joensuu, a friendly city “in the middle of KNOWhere” (as one of UEF’s slogans say) of about 115,000 inhabitants, is compact and contains all the necessary services within walking distance, with low living expenses and excellent opportunities for many outdoor activities. Despite its remote location, Joensuu is very international thanks to many of UEF’s international collaboration programmes and a vibrant student community.  

A person to be appointed as a postdoctoral researcher shall hold a suitable doctoral degree that has been awarded less than five years ago. If the doctoral degree has been awarded more than five years ago, the post will be one of a project researcher. The doctoral degree should be in spoken language technology, electrical engineering, computer science, machine learning or a closely related field. Researchers finishing their PhD in the near future are also encouraged to apply for the positions. However, they are expected to hold a PhD degree by the starting date of the position. We expect strong hands-on experience and a creative, out-of-the-box problem solving attitude. A successful applicant needs to have an internationally proven track record in topics relevant to the project he or she applies to.

English may be used as the language of instruction and supervision in these positions.

The positions will be filled from earliest April 1, 2019 for a minimum period of 12 months. The continuation of the positions will be agreed separately. The positions will be filled for a fixed term due to them pertaining to a specific project (positions of postdoctoral researcher shall always be filled for a fixed term, UEF University Regulations , Section 31).

The salary of the positions is determined in accordance with the salary system of Finnish universities and is based on level 5 of the job requirement level chart for teaching and research staff (€2,903.61/ month). In addition to the job requirement component, the salary includes a personal performance component, which may be a maximum of 50.0% of the job requirement component. The salary of a postdoctoral researcher is in the beginning around 3,300.00 - 3,500.00 euros.

For further information on the position, please contact (NOTCH): Associate Professor Tomi Kinnunen, email: tkinnu(a)cs.uef.fi, tel. +358 50 442 2647. For further information on the application procedure, please contact: Executive Head of Administration Arja Hirvonen, email: arja.hirvonen(a)uef.fi, tel. +358  29 445 3002.

A probationary period is applied to all new members of the staff.

You can use the same electronic form to apply for both research projects. The electronic application should contain the following appendices:

  • a résumé or CV
  • a list of publications
  • copies of the applicant's academic degree certificates/ diplomas, and copies of certificates / diplomas relating to the applicant’s language proficiency, if not indicated in the academic degree certificates/diplomas
  • motivation letter

The application needs to be submitted no later than February 28, 2019 (by 24:00 EET) by using the electronic application form.

Link to the application form: https://rekry.saima.fi/certiahome/aew.html?did=5600&lang=fi&job_id=6762&jc=16

 



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