Aalto University Aalto University is a community of bold thinkers where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto University has been ranked the 9th best young university in the world (Top 50 under 50, QS 2018) and one of the world’s top technology challenger universities (THE 2017), thinking outside the box on research collaboration, funding and innovation. Aalto has six schools with nearly 11 000 students and 4000 employees of which close to 400 are professors. Our campuses are located in the Capital Area of Finland. With 37% of our academic faculty coming from outside Finland, we are a highly international community with strong academic standing.
 
At Aalto, high-quality research, art, education and entrepreneurship are promoted hand in hand. Disciplinary excellence is combined with multidisciplinary activities, engaging both students and the local innovation ecosystem. Our main campus is quickly transforming into an open collaboration hub that encourages encounters between students, researchers, industry, startups and other partners. Aalto University was founded in 2010 as three leading Finnish universities, Helsinki University of Technology, the Helsinki School of Economics and the University of Art and Design Helsinki, were merged to strengthen Finland’s innovative capability.
 Aalto University School of Electrical Engineering invites applications for an 

 ASSOCIATE OR ASSISTANT PROFESSOR IN SPEECH AND LANGUAGE TECHNOLOGY
 
We are looking for an associate or assistant professor to establish and lead a group of researchers and students within the speech and language technology research area at Aalto University. In this position, you will have a chance to make an impact in academic research and teaching as well as in society for example by industrial research collaboration. At Aalto University, you will have excellent research facilities and opportunities for interdisciplinary research with top-level researchers in machine learning, signal processing, acoustics, neuroscience and human-computer interfaces. 
 YOUR ROLE AND GOALS Your tasks and responsibilities include conducting outstanding research and teaching as well as preparing research projects with funding from international and national sources. You supervise and recruit postdocs and PhD students and participate in teaching within the Computer, Communication and Information Science Master's Programme which is one of the most competitive at Aalto University. 
 SCIENTIFIC ENVIRONMENT The professorship is situated at the Department of Signal Processing and Acoustics (in School of Electrical Engineering) where currently two out of ten tenured/tenure track professors work in the area of speech and language technology. Academy Professor Paavo Alku leads a group in speech communication technology and text-to-speech synthesis. The group is known particularly for its voice source research and its technical and interdisciplinary applications. Professor Mikko Kurimo leads a group in speech recognition and language modeling. His group is best known for developing successful language-independent models of morphologically rich languages and winning the MGB 2017 speech recognition challenge. These groups are also very well connected to the recently founded Finnish Centre of Artificial Intelligence (FCAI), which is a large collaboration effort for professors in machine learning and speech and language technology in both Aalto University and University of Helsinki. 
 YOUR EXPERIENCE AND AMBITIONS We expect a strong track record of publications and achievements in speech and language technology, excellent teaching skills to help students to learn difficult topics, and motivation and competence to start and lead new and highly ambitious research projects aiming at significant scientific results and impacts. The professorship is open for qualified applicants from all areas of speech and language technology but we prioritize such fields, which enable research collaboration with the Department’s current groups in speech and language technology.
All applicants must have a doctorate in speech and language technology (or in a related area of engineering) and fluent command in English. 
 
If you wish to hear more about the position, you can contact Academy Professor Paavo Alku or Professor Mikko Kurimo (firstname.lastname@aalto.fi). In recruitment process related questions, please contact HR Coordinator Saara Haggrén (firstname.lastname@aalto.fi). 
 READY TO APPLY? If you want to join our community, please submit your application through our eRecruitment system no later than 31 March 2019. 
 To apply, please share the following application materials with us:
 1. Cover letter 2. Curriculum vitae (with contact information and ResearcherID number) 3. List of publications in which the 7 most significant publications are highlighted 4. A research statement describing past research and plans for future research  5. A teaching portfolio describing teaching experience and plans for teaching 6. Contact information of possible references or at most 2 reference statements
 
All application materials should be submitted in English, in pdf format. The applications for the tenure track positions are to be addressed to the President of Aalto University. 
 
From amongst the applicants in the first phase, Aalto University will select those who will be asked to visit Aalto University in May/June 2019. 
 
Short-listed candidates’ applications will be submitted for review by external experts (the second phase of the application process). 
 
General instructions for applicants including evaluation criteria, language requirements and guidelines for compiling teaching portfolio and CV are given at https://www.aalto.fi/tenure-track.  
 
Aalto University reserves the right for justified reasons to leave the position open, to extend the application period and to consider candidates who have not submitted applications during the application period.
 
 
 
As a living and working environment, Finland consistently ranks high in quality-of-life. For more information about living in Finland: https://www.aalto.fi/services/about-finland.

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 Mines Nancy et le LORIA recrutent un MCF section 27 avec un profil IA / Deep Learning
théorique ou appliqué (parole, texte, image, etc):
http://www.loria.fr/wp-content/uploads/2018/12/Fiche-de-poste-MCF-27-0416-Mines-LORIA.pdf

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The Department of Computer Science at the University of Colorado Boulder anticipates hiring a full time postdoctoral fellow starting in Summer/Fall 2019 for one year and renewable for a second year. This position will work with Dr. Sidney D?Mello https://www.colorado.edu/ics/sidney-dmello and will play a collaborative and co-leadership role in a vibrant research team encompassing researchers in Computer Science, Cognitive Science, Psychology/Neuroscience, and Education.

Who we are:

=========

The mission of the Institute of Cognitive Science (ICS) at CU-Boulder is to understand and enhance human cognition, learning, and development through the creation of interdisciplinary partnerships.  ICS fosters rich scientific interchange across researchers from a broad range of disciplines including Artificial Intelligence, Linguistics, Psychology, Neuroscience, Computer Science, Philosophy, and Education.

What your key responsibilities will be:

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

Develop computational modeling and machine learning techniques to model behavioral and mental states (e.g., affect, attention, workload) from multimodal data (e.g., video, audio, physiology, eye gaze) across a range of interaction contexts (e.g., learning from educational games, collaborative problem solving, everyday activities in the wild).

This position offers a unique postdoctoral training experience and unsurpassed publishing opportunities within multi-department and multi-institution grant-funded projects. The candidate will be encouraged to develop advanced technical skills, strengthen their research portfolios via peer-reviewed publications, gain interdisciplinary experience by working with a diverse team, develop leadership skills by mentoring students, and gain expertise in co-authoring grant proposals.

Requirements:

===========

- Ph.D. in Computer Science/Machine Learning or a related field (at the time of hire)

- Research experience in computational modeling and advanced machine learning (e.g., graphical models, deep recurrent neural networks).

- Strong writing skills and ability to conduct independent research

as evidenced by first author publications.

Desired:

- Research experience in one or more of the following areas (computer vision, computational psychophysiology, natural language processing, speech processing).

- Background in interdisciplinary research.

- Experience mentoring graduate and undergraduate students.

Job details:

========

- 1-2 year position. Initial contract is for one year (providing renewal after 6-month probationary period). Second year contract is based on performance and extension to a third year is possible.

- Start date is negotiable, but anticipated for Summer/Fall 2019.

- Competitive salary commensurate with experience and full benefits.

Application:

=========

To apply, please submit the following materials through CU Boulder Jobs:

- Resume/CV

- Cover Letter

- PDF Sample of Work: Two representative publications.

During the application process you will need to enter contact information for three references and we will request letters of recommendation and additional materials, if needed, as the search progresses.

Review of applications will begin immediately and will continue until the position is filled.

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

Be Accomplished. Be Resourceful. Be Boulder.

https://www.colorado.edu/ics/

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Postdoc in Speech Production (M/F)

Reference : UMR7018-CECFOU-004
Workplace : PARIS 05
Date of publication : Monday, January 07, 2019
Type of Contract : FTC Scientist
Section CN : Sciences du langage
Contract Period : 12 months
Expected date of employment : February/March 2019
Proportion of work : Full time
Remuneration : between 2600 et 3600? (brut) per month according to experience
Desired level of education : PhD
Experience required : Indifferent

Missions

The post-doctoral fellow will conduct experiments on adaptation to speech disturbances and production conditions, aimed at testing the integrity/flexibility of speech units and their variability according to their structural/motor complexity or frequency. Speech will be compared with other non-verbal movements and data from healthy speakers will be compared with data from speakers with different motor speech disorders.

Activities

As part of the MoSpeeDi project, the Laboratoire de Phonétique et Phonologie (LPP - CNRS/Sorbonne Nouvelle) in Paris is offering a full-time postdoctoral position for 12 months (with a possible extension of a few months).
The post-doctoral fellow will be in charge of designing, carrying out and processing experiments in collaboration with the other members of the project.
The overall objective of the project is to better understand the processes and representations at play during speech production, focusing on the final stages of the process where the encoded linguistic message is transformed into articulated speech. At the interface between linguistic and motor processes, these steps are also associated with various Motor Speech Disorders (MSD, dysarthria and speech apraxia). Articulation and acoustic data will be collected and analyzed experimentally for healthy and MSD speakers to (a) better understand the phonetic and motor speech planning and programming stages, (b) identify markers of these processes, and (c) better isolate and categorize speech disorders in MSDs.

Skills

Required skills

- PhD in phonetics or on a subject related to speech production or speech motor control.
- Good knowledge of speech production and motor control models, particularly on adaptation phenomena and/or speech motor disorders.
- Experience in signal processing (acoustic and/or articulatory)
- Programming skills (e. g. with Praat, Matlab, Python or R)
- Strong statistical analysis skills and good writing skills
- Basic knowledge of French and excellent proficiency in English

Work Context

The candidate will work closely with Cécile Fougeron and Leonardo Lancia.
The Phonetics and Phonology Laboratory (CNRS/Université Sorbonne-Nouvelle), located in the 5th arrondissement in Paris, is a research, research training and teaching unit in experimental phonetics and phonology.

Application process and contact information

Applications can be submitted online at the following address: http://bit.ly/2C7LXSh. For more information, the candidates can contact Cécile Fougeron (cecile.fougeron@sorbonne-nouvelle.fr) and Leonardo Lancia (leonardo.lancia@sorbonne.nouvelle.fr).

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IDIAP has openings for at least one post-doc in the general area of speech and
natural language processing.  The work will involve investigating how to interface
current end-to-end speech recognition technology with its counterparts in natural
language processing; it would suit someone from either discipline.  The posts are
research oriented, but funded by industrial collaborations.
More information along with application instructions are at the URL:
 http://www.idiap.ch/education-and-jobs/job-10251

Idiap is located in Martigny in French speaking Switzerland, although the lab hosts many
nationalities, and functions in English.  All positions offer quite generous salaries.

Several similar positions at PhD, post-doc and senior level are available at the
institute in general.
 http://www.idiap.ch/en/join-us/job-opportunities

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Naples (Italian: Napoli) is the third largest city in Italy, and is the capital of the Campania region. World-known for its rich history, art, culture, architecture, music, and gastronomy, Naples is a lively, exciting and bustling city situated on the southwest coast in a gorgeous gulf, and is surrounded by attractive tourist and archaeological sites such as Capri, Ischia, Amalfi Coast, Pompei, Ercolano, Mount Vesuvius. See https://www.visitnaples.eu/en for further information.

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Sujet de stage M2 : sleepiness detection and characterization in voice recordings.

Advisors: - Jean-Luc Rouas – CR CNRS, LaBRI : rouas@labri.fr

- Pr. Pierre Philippe – PU-PH, SANPSY : pr.philip@free.fr

Subject: Detection sleepiness is useful for many reasons : for instance, it can help prevent road traffic accidents, it can be useful to monitor workers in critical environments (air traffic control, nuclear plants, etc.). While these applications are very important, it can also be used in a clinical way in the follow-up of sleep deprived patients. The Obstrusive Sleep Apnea is nowaday recognised as a major public health problem resulting in many consequences : road traffic accidents, increase in heart failure rates, behavioural and cognitive troubles, … In order to deal with these problems, we devised an experiment with the SANPSY research unit (Sommeil - Addiction- Neuropsychiatrie) Université Bordeaux Ségalen CNRS USR 3413) in order to assess if we can evaluate the sleepiness level of a patient using only a simple speech recording. Previous research has shown that this task is possible, however most studies on sleepiness detection from speech rely on corpora with self reported labels according to the KSS scale [1]. For instance, the Interspeech 2011 speaker state challenge [2] uses data from 99 speakers and contains mixed data from different tasks (isolated vowels, read speech, command request, spontaneous speech) in German. The annotations are self-reported using the KSS scale and are divided in two classes : sleepy (S) and not sleepy (NS). The best system [3] in the challenge competition won with a reported accuracy slightly above the baseline, around 72 % of correctly identified samples. Other efforts on sleepiness detection from speech often use the same kind of data. For example, in [4] 77 participants are recorded speaking isolated vowels, and the annotation is also made using self-reported scores from the KSS scale. Reported performances on two classes (S and NS) are around 78 % of correction identification.  In a more recent paper [5], the number of participants is increased (402), the recordings are read passages from 7 texts. However the classification task is not the same since the classifier tries to predict the value of the KSS score. In our project, in close partnership with the SANSPY unit, we started to record patients (current number of patients recorded is 78) while asssessing their sleepiness states by various measurements including EEG as well as clinical expertise. Recording the patients follows a strict clinical methodology resulting in sets of 4 recordings per patient, always at the same time of the day. Three categories of sleepiness level have been devised according to the health professionals (instead of usually two in previous research on sleepiness detection in speech): very sleepy, intermediate and normal. Using these recordings and the provided categories, we begun to test different features and classification methods. Using a relatively small set of features and simple classification techniques, we managed to obtain in a cross validation procedure a global classification rate of 70% correct. The task of the intern student is to further explore the different possibilities in terms of features and machine learning methods as the data collection continues, and to carry on thorough analysis of
the results so as to understand the influence of several factors such as  gender, age, or pathology.

References:

[1] Shahid, A., Wilkinson, K., Marcu, S., & Shapiro, C. M. (2011). Karolinska sleepiness scale (KSS). In STOP, THAT and One Hundred Other Sleep Scales (pp. 209-210). Springer New York.

[2] Schuller, B.; Steidl, S.; Batliner, A.; Schiel, F.; Krajewski, J.: “The Interspeech 2011 Speaker State Challenge”, Interspeech (2011), ISCA, Florence, Italy, 2011.

[3] Dong-Yan Huang, Zhengchen Zhang, Shuzhi Sam Ge, Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines, In Computer Speech & Language, Volume 28, Issue 2, 2014, Pages 392-419, ISSN 0885-2308, https://doi.org/10.1016/j.csl.2013.06.002.

[4] Krajewski, J., Schnieder, S., Sommer, D., Batliner, A., & Schuller, B. (2012). Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech. Neurocomputing, 84, 65-75.

[5] Krajewski, J., Schnieder, S., Monschau, C., Titt, R., Sommer, D., & Golz, M. (2016, October). Large Sleepy Reading Corpus (LSRC): Applying Read Speech for Detecting Sleepiness. In Speech Communication; 12. ITG Symposium; Proceedings of (pp. 1-4). VDE.

Requested skills:

- speech processing and/or signal processing techniques

- machine learning

- programming languages : matlab, python, C/C++

- interest in clinical research and/or cognitive sciences

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Post-doctoral positions at Telecom-ParisTech on Deep learning approaches for social computing in human-agent interactions

*Place of work* Telecom ParisTech [TPT] 46 rue Barrault  75013 Paris ? France Paris until september 2019, and then Palaiseau (Paris outskirt)

*Starting date* From now to September 2019

*Salary* according to background and from 2300 ? /month

*Duration* 12 months renewable

*Context*
The post-doctoral fellowship will take part in the Telecom-ParisTech?s chair on Data Science & Artificial Intelligence for Digitalized Industry & Services [DSIAI]. Established for a five-year period, one of its main goals of the chair is to allow sustainable funding of research activities in AI and Data Science, on methodological topics crucial for applications.
The research activity of the postdoctoral fellowship will also contribute to the Social Computing topic [SocComp.] of the S2a team [SSA] at Telecom-ParisTech, in close collaboration with other researchers and PhD students of the team.

* Candidate profile*
As a minimum requirement, the successful candidate should have:
? A PhD in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, affective computing, reinforcement learning, natural language processing, speech processing
?    Excellent programming skills (preferably in Python)
?    Excellent command of English

*How to apply*
The application should be formatted as **a single pdf file** and should include:
?    A complete and detailed curriculum vitae
?    A letter of motivation
?    The defense and Phd reports
?    The contact of two referees
The pdf file should be sent to the two supervisors: Chloé Clavel [Clavel] and Giovanna Varni [Varni]: chloe.clavel@telecom-paristech.fr, giovanna.varni@telecom-paristech.fr


1/ First position: Multimodal attention models for predicting the user's socio-emotional behavior in human-machine interactions

*Keywords* human-machine interaction, attention models, recurrent neural networks, Social Computing, natural language processing, speech processing, multimodality

*Supervision* Chloé Clavel,  Giovanna Varni,

*Description* Social robotics, and more broadly human-agent interaction, is a field of human-machine interaction for which the integration of socio-emotional behaviors (emotions, social attitudes, personality) is expected to have a great potential. For example, companion robots are designed to provide their users with both help (especially in the assistance and dependency market) and entertainment (in the entertainment market). For intelligent cars, the analysis of the driver's emotions through multimodal sensors can provide a better understanding of his driving [CARS]
This post-doctoral fellowship will focus on multimodal modeling for the prediction of the user's socio-emotional behaviors during interactions with a virtual agent.   In particular, the post-doctoral fellow will address the following points:
- the encoding of multimodal representations relevant for the modelling of socio-emotional behavior;
- the development and evaluation of models that take advantage of the complementarity of modalities in order to monitor the evolution of the user's socio-emotional behaviors during the interaction (e. g. taking into account the inherent sequentially of the interaction structure)
The models will be based on sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou] and [BenYoussef].


2/ Second Position: Reinforcement learning for the development of socially competent agents

*Keywords* human-machine dialogue, reinforcement learning, language generation model, Social Computing

*Supervision* Chloé Clavel

*Description* Conversational agents (e.g. Djingo, Orange, Alexa d'Amazon, Siri d'Apple, Cortana de Microsoft, etc.), chatbots and more broadly human-agent interaction and social robotics (see for example [CIMON]) are applications for which the integration of socio-emotional behaviour analysis in the interaction between humans and virtual agents has great potential. Recent developments in artificial intelligence in natural language processing have made it possible to set up functional chatbots: extraction of keywords, understanding of natural language, question and answer systems, dialogue trees. While virtual assistants are already on the market, taking into account the social component of interaction remains a crucial issue for the fluidity and naturalness of interaction. For example, the development of socio-emotional interaction strategies can compensate for the chatbot's lack of understanding of user requests, which results in expressions of frustration and irritation on the part of the user [Maslowski] and can lead to the user abandoning the conversation (also called an engagement breakdown [BenYoussef]), thus hindering the completion of the chatbot's intended task.
This post-doctoral fellowship will address this issue - the development of socially competent agents - by proposing methods of reinforcement and deep learning [Qureshi, Ritschel] for the selection and generation of natural language utterances based on their socio-emotional relevance.

Selected references of the team:
[Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews.  in AAAI 2019, to appear
[Garcia] Alexandre Garcia, Chloé Clavel, Slim Essid , Florence d?Alche-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, ICML 2018
[Clavel&Callejas] Clavel, C.; Callejas, Z., Sentiment analysis: from opinion mining to human-agent interaction, Affective Computing, IEEE Transactions on, 7.1 (2016) 74-93.
[Langlet] C. Langlet and C. Clavel, Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user?s likes and dislikes , in ACL 2015
[Maslowski]  Irina Maslowski, Delphine Lagarde, and Chloé Clavel.  In-the-wild chatbot corpus: from opinion analysis to interaction problem detection, ICNLSSP 2017.
[Ben-Youssef]  Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim.  Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions.  In  Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464?472. ACM, 2017.

Other references:
[DSIAI] https://datascienceandai.wp.imt.fr/
[TPT] https://www.telecom-paristech.fr/eng/ 
[SocComp.] https://www.tsi.telecom-paristech.fr/recherche/themes-de-recherche/analyse-automatique-des-donnees-sociales-social-computing/
[SSA] http://www.tsi.telecom-paristech.fr/ssa/#
[Clavel] https://clavel.wp.imt.fr/publications/
[Varni] https://sites.google.com/site/gvarnisite/
[CARS] https://www.lesfurets.com/assurance-auto/actualites/voiture-intelligente-decrypter-emotions
[CIMON] http://blogs.esa.int/alexander-gerst/2018/11/16/alexander-welcomes-cimon/
[Qureshi]  Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, and Hiroshi Ishiguro.  Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning. Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on. IEEE, 2016.
[Ritschel] Ritschel, Hannes, and Elisabeth André. 'Real-time robot personality adaptation based on reinforcement learning and social signals.' Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 2017.

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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 la 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/mars 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
Profil recherché : titulaire d'un doctorat en informatique, machine learning, traitement de l'audio.
Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr) 
Dossier de candidature : à envoyer avant le 15 février 2019.
Détail de l'offre :  https://www.irit.fr/recherches/SAMOVA/pagejobs.html
Salaire : selon expérience

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Le département informatique de l?UFR Sciences de l?université
Paris-Sud recrute un.e Maître de Conférences pour renforcer son équipe
pédagogique et continuer de développer la recherche au sein du LIMSI
sur les les thèmes du traitement automatique des langues et de la
parole.

Les recherches de la personne recrutée porteront en priorité sur le
développement de nouvelles méthodes en traitement automatique de la
parole, avec par exemple les thématiques suivantes: la caractérisation
du locuteur dans un contexte multimédia ; l?étude des dimensions
affectives des interactions sociales ; l?étude des systèmes de
traduction automatique et l?apprentissage artificiel ; l?étude des
systèmes de reconnaissance vocale. Le laboratoire est également ouvert
à des candidatures qui mettraient en avant d?autres thématiques
relatives au traitement automatique de la parole, ou plus largement à
l?ensemble du champ du traitement automatique des langues.

La personne recrutée pourra enseigner dans toutes les filières
relevant du département informatique de l'UFR Sciences d?Orsay, au
niveau Licence et Master (classique et en apprentissage). Elle pourra
enseigner dans ses domaines d?intérêt et dans un ou desles domaines de
l'informatique qui auront besoin d'être renforcés. La personne pourra
également dispenser une partie de ses enseignements en anglais, en
particulier dans le cadre de Masters internationaux.

Les candidat.e.s pourront obtenir des informations plus précises à
partir de la page suivante:

https://www.limsi.fr/fr/limsi-emplois/offres-de-postes-chercheurs-et-enseignants-chercheurs

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Fully funded PhD and PostDoc positions are available at the Casa Paganini -
InfoMus Research Centre (www.casapaganini.org), DIBRIS-Dept. of Informatics,
Bioengineering, Robotics, and Systems Engineering, Polytechnic School,
University of Genoa, Italy. Each research position will have a specific
focus on the development of computational models, multimodal systems and
interfaces, research experiment and prototypes in one of the following
areas: (i) automated measurement, analysis, and prediction of full-body
non-verbal individual movement qualities and emotions; (ii) automated
measurement, analysis, and prediction of full-body non-verbal social signals
(synchronization, entrainment, leadership).
Accepted candidates will develop a research plan in the framework of the
4-year (2019-2022) Horizon 2020 European Project FET Proactive EnTimeMent
(https://entimement.dibris.unige.it/ ), and may be asked to participate in
joint activities with research partners in EnTimeMent, including possible
short residencies at EnTimeMent partners' sites.

Requirements
Candidates should ideally have the following profile:
+ Master's degree in Computer Science, Computer Engineering or related
disciplines;
+ Excellent technical and programming skills (Python, Java, C/C++);
+ Prior experience in at least one of the following fields: human computer
interaction, affective computing, motion capture and motion analysis,
multimodal interfaces, sound analysis and interactive sonification, computer
vision, machine learning;     
+ Ability to work independently, self-motivation, and ability to actively
contribute as a member of a multidisciplinary research team including
experts in computer science and engineering, movement science, cognitive
neuroscience, cognitive and motoric rehabilitation, performing arts;
+ Strong commitment to advancing the state-of-the-art research and
publishing in top research venues;
+ Excellent communication skills in English.
 
Applying
To apply, please email your application to: antonio.camurri@unige.it and
gualtiero.volpe@unige.it
 The application should consist of a single pdf file including:
+ cover letter expressing your interest in the position and your profile
relevance;
+ curriculum vitae showing academic records with tracks related to the
themes of the thesis;
+ list of publications (post-doc applications only);
+ transcript of marks according to M1-M2 profile or last 3 years of
engineering or related school (PhD applications only);
+ contact and recommendation letter of at least two university referents;

As a preliminary step, candidates will be invited for a Skype interview.
Candidates may also be invited to a fully funded short research internship
in our research team during summer 2019. To be finally enrolled, candidates
will need to pass a formal evaluation performed by a selection committee
University of Genova will appoint according to the Italian laws.
The envisioned starting date for the first selected PhD candidates is
November 2019. PostDoc starting date is negotiable.

Conditions of employment     
Hired on a fixed-term contract at University of Genoa, working full-time at
the Casa Paganini-InfoMus Research Centre of DIBRIS, University of Genoa,
with possible short internships at a research centre of an EnTimeMent
project partner.
Duration: three years for PhD students; 2-year contract for post-docs
(possible extensions available).

Further Information     
For any question or further details please send email to
antonio.camurri@unige.it and gualtiero.volpe@unige.it

The Casa Paganini-InfoMus Research Centre at DIBRIS, Polytechnic School,
University of Genoa, Italy
As art influences science and technology, science and technology can in turn
inspire art. Recognizing this mutually beneficial relationship, researchers
at the Casa Paganini-InfoMus Research Centre work to combine scientific
research in information and communications technology (ICT) with artistic
and humanistic research.
The mission of Casa Paganini - InfoMus consists of carrying out scientific
and technological research on human-centered computing where art and
humanistic culture are a fundamental source of inspiration. The research
team includes computer engineers and experts from the human sciences and the
arts.
Scientific and technological research includes: investigation and
development of computational models and of multimodal systems focusing on
non-verbal, full-body, expressive, emotional, and social behavior
(entrainment, leadership); sound and music computing; interactive
sonification; multimodal interactive systems and serious games for
rehabilitation, entertainment, sport, edutainment, museums and cultural
institutions; multimedia systems and services for the creative industry: ICT
for active music listening, interactive dance, theatre, cultural heritage,
user-centric media and mobile systems.
The Casa Paganini - InfoMus Research Centre coordinates and participates as
partner in many international projects on scientific and technological
research, education, and develops multimedia systems, platforms, and
applications for the creative industry and cultural institutions.
www.casapaganini.org                youtube.com/InfoMusLab

The EnTimeMent EU Horizon 2020 FET PROACTIVE project
EnTimeMent aims at a radical change in scientific research and enabling
technologies for human movement qualitative analysis, entrainment and
prediction, based on a novel neuro-cognitive approach of the multiple,
mutually interactive time scales characterizing human behaviour. Our
approach will afford the development of computational models for the
automated detection, measurement, and prediction of movement qualities from
behavioural signals, based on multi-layer parallel processes at non-linearly
stratified temporal dimensions, and will radically transform technology for
human movement analysis. EnTimeMent new innovative scientifically-grounded
and time-adaptive technologies operate at multiple time scales in a
multi-layered approach: motion capture and movement analysis systems will be
endowed with a completely novel functionality, achieving a novel generation
of time-aware multisensory motion perception and prediction systems. The
proposed model and technologies will be iteratively tested and refined, by
designing and performing controlled and ecological experiments, ranging from
action prediction in a controlled laboratory setting, to prediction in
dyadic and small group interaction. EnTimeMent scenarios include health
(healing and support of everyday life of persons with chronic pain and
disability), performing arts (e.g. dance), sports, and entertainment group
activities, with and without living architectures. EnTimeMent will create
and support community-building and exploitation with concrete initiatives,
including a community of users and stakeholders, innovation hubs and SME
incubators, as premises for the consolidation beyond the end of the project
in a broader range of market areas.
http://entimement.dibris.unige.it

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Graz University of Technolgy (TU Graz) is the organizer of the INTERSPEECH 2019 conference in September 2019 and offers a PhD position in its Signal Processing and Speech Communication Laboratory. Be part of this exciting opportunity and join our team!

The position is for up to four years and involves both research and teaching commitments. Teaching will be focussed on problem classes and lab courses for fundamental subjects such as signal processing. Research will address interdisciplinary topics at the interface between automatic speech recognition and speech science. You will work on top-level publications and your PhD thesis under the joint supervision of Prof. Gernot Kubin and Dr. Barbara Schuppler. Graz University of Technology offers systematic guidance to their doctoral students in specific doctoral schools with structured programs, international cooperation opportunities, and more. All doctoral programs and more than half of our Masters' programs are taught in English. The gross salary (before taxes) for this full-time position is according to scale B1 at Austrian Universities, approximately 40.000,- EUR per year. The expected starting date is September-October 2019.

Mandatory skills of the candidates are a relevant master's degree in electrical or information engineering, computer science, or speech science; excellent programming skills; English language competence (IELTS 7.0 or higher). Expertise in signal processing and machine learning as well as knowledge of the German language are considered additional assets.

Interested candidates should send immediately the following information in PDF format to Prof. Gernot Kubin (g.kubin@ieee.org) and to the dean’s office dekanat.etit@tugraz.at: curriculum vitae, transcript of records of both Bachelor's and Master's degree courses, master's thesis and all publications, proof of English language competence, and contact information for 2 referees. The official application deadline is July 26, 2019. Additional application documents may be required in due course. Female students are particularly encouraged to apply. For more information consult

https://www.spsc.tugraz.at  for the Signal Processing and Speech Communication Laboratory,
https://www.interspeech2019.org  for the INTERSPEECH 2019 conference, https://www.tugraz.at/tu-graz/arbeiten-an-der-tu-graz/offene-stellen/ausgeschriebene-wissenschaftliche-stellen-ausser-professuren/#c57834 for the official job announcement in German,
https://www.tugraz.at/en/go/welcome-center  for information on student life at Graz University of Technology and in the city of Graz, Austria.

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Faculty position (Associate professor) at Telecom ParisTech in 

Machine-Learning.

Important Dates 

?    May 3, 2019: closing date

?    June 3, 2019: hearings of preselected candidates

Telecom ParisTech?s [1] machine learning, statistics and signal processing group (a.k.a S²A group) [2], within the laboratoire de traitement et communication de l?information (LTCI) [5], is inviting applications for a permanent (indefinite tenure) faculty position at the *Associate Professor* level (Maitre de Conferences) in *Machine learning*.

Main missions

The recruit will be expected to:

Research activities

?    Develop groundbreaking research in the field of theoretical or applied machine learning,   targeting applications that are well aligned with the topics of the S²A group [3] and the Images, Data & Signals department [4], which include (and is not restricted to) sequential/reinforcement learning, multitask learning,  learning for structured data (e.g. time series analysis, audio signals), natural language processing, social signal processing, predictive maintenance, biomedical or physiological signal analysis, recommendation, finance, health, ?.  

?    Develop both academic and industrial collaborations on the same topic, including collaborative activities with other Telecom ParisTech research departments and teams, and research contracts with industrial players

?    Set up research grants and take part in national and international collaborative research projects

Teaching activities 

?    Participate in teaching activities at Telecom ParisTech and its partner academic institutions (as part of joint Master programs), especially in machine learning and Data science, including life-long training programs (e.g. the local Data Scientist certificate) 

Impact

?    Publish high quality research work in leading journals and conferences

?    Be an active member of the research community (serving in scientific committees and boards, organizing seminars, workshops, special sessions...)

Candidate profile

As a minimum requirement, the successful candidate will have:

?    A PhD degree

?    A track record of research and publication in one or more of the following areas: machine learning, applied mathematics, signal processing,

?    Experience in teaching

?    Good command of English

The ideal candidate will also (optionally) have:

?    Experience in temporal data analysis problems (sequence prediction, multivariate time series, probabilistic graphical models, recurrent neural networks...)

NOTE:

The candidate does *not* need to speak French to apply, just to be willing to learn the language (teaching will be mostly given in English)

Other skills expected include: 

?    Capacity to work in a team and develop good relationships with colleagues and peers

?    Good writing and pedagogical skills

More about the position

?    Place of work: Paris until 2019, then Saclay (Paris outskirts)

?    For more information about being an Associate Professor at Telecom ParisTech, check [6] (in French)

 How to apply

Applications are to be sent by e-mail to: recrutement@telecom-paristech.fr

The application should include:

?    A complete and detailed curriculum vitae

?    A letter of motivation

?    A document detailing past activities of the candidate in teaching and research: the two types of activities will be described with the same level of detail and rigor.

?    The texts of the main publications

?    The names and addresses of two referees 

?    A short teaching project and a research project (maximum 3 pages)

Contacts :

Stephan Clémençon (Head of the S²A group)

Gaël Richard (Head of the IDS department)

[1] http://www.tsi.telecom-paristech.fr

[2] http://www.tsi.telecom-paristech.fr/ssa/ 

[3] http://www.tsi.telecom-paristech.fr/aao/en/  

[4] http://www.tsi.telecom-paristech.fr/en/
[5] https://www.ltci.telecom-paristech.fr/?lang=en
[6] http://www.telecom-paristech.fr/telecom-paristech/offres-emploi-stages-theses/recrute-enseignants-chercheurs.html 

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PhD student “Morphology in spoken word recognition models”

Location: Radboud University, Nijmegen

Supervision: Louis ten Bosch, Mirjam Ernestus, and Ingo Plag

Starting date: September / October 2019

Duration: 4 Years (3 years, with possibility of extension of 1 year)

Salary: Around 1600 euros a month

The project is part of the project DMC: Dutch morphologically complex words: The role of morphology in speech production and comprehension of the Spoken Morphology: Phonetics and phonology of complex words DFG research unit FOR 2373. It is funded by the Deutsche Forschungsgemeinschaft.

See http://www.spoken-morphology.hhu.de/en.html

Requirements:

We are looking for candidates with

• Master degrees in Linguistics, Artificial Intelligence, or Social Sciences;

• Clear interest in speech and language;

• Expertise in quantitative research methods, preferably including large scale data analyses or computational modeling;

• Effective verbal and written communicative skills in English.

Application:

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

The deadline is 15 March 2019 or until the position is filled.

Questions:

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

Abstract:

The PhD student will study what properties a computational model of auditory word recognition needs to have in order to well simulate human listeners’ processing of morphologically complex words. The computational models that will be considered are DIANA (e.g., ten Bosch et al., 2013, 2014, 2015) and Naïve Discriminative Learning (NDL, e.g., Arnold et al. 2017), since these two models represent very different types of processing and both can receive the speech signal, with morpho-acoustic cues, as their inputs. The human data to be simulated will include the BALDEY database (Ernestus & Cutler, 2015). The PhD student will produce a dissertation consisting of several publishable articles, preceded by a General Introduction and followed by a general discussion.

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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 : March 15th, 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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 SHEFFIELD CENTRE FOR DOCTORAL TRAINING IN SPEECH AND LANGUAGE

The University of Sheffield has won an £8M grant to fund some 60 PhDs over a period of 8
years in Speech and Language Technology. This 'Centre for Doctoral Training' is one of 16
newly announced by UKRI (UK Research and Innovation) to strengthen British AI research.

The Sheffield CDT will cover a wide range of SLT subjects, researching and developing
robust methods for natural language and speech processing in challenging real world
scenarios. Its research will stimulate novel applications of SLTs in sectors such as
health, sport, manufacturing, aerospace, robotics, finance, political science and digital
humanities.

A major feature of the CDT is close collaboration with industry, involving multinationals
such as Google, Amazon, Voicebase, Nuance, NHS Digital, Solvay and TechNation, as well as
substantial UK SME support.

In addition to a Ph.D., students will complete a Postgraduate Diploma (PGDip) in SLT
Leadership. This bespoke programme will provide them with the necessary skills for
academic and industrial leadership in SLT, and will cover software engineering,
entrepreneurship, management, and societal responsibility.

The SLT is headed by Professor Thomas Hain and Professor Rob Gaizauskas.

For more details, visit

https://www.sheffield.ac.uk/dcs/latest-news/speech-and-language-centre-for-doctoral-training-1.831895

https://slt-cdt.group.shef.ac.uk/

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En collaboration avec les directions des trois plateformes technologiques1 CRVM, CEP et H2C2, et sous la responsabilité du chargé de mission Plateformes technologiques d?Aix-Marseille, le/la chargé.e d?affaires conçoit et met en oeuvre la stratégie de développement des

offres de services des plateformes technologiques afin de favoriser leur croissance.

 Date limite de candidature 23 avril 2019

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CRIM (COMPUTER RESEARCH INSTITUTE OF MONTREAL) IS HIRING! 

POSTDOCTORAL RESEARCHER POSITION - SPEAKER RECOGNITION Speech and Text group
 
CRIM (http://www.crim.ca/en) is an applied research and expertise centre in information technology, dedicated to making organizations more effective and competitive through the development of innovative technology and the transfer of leading edge know-how, while contributing to scientific advancement.
 
JOB DESCRIPTION
CRIM is looking for a postdoctoral researcher with a background in speaker recognition, and, ideally, in other related fields such as speaker diarization, speech recognition and machine learning. The successful candidate will work on speaker recognition RD activities within the Speech and Text group.
 
The ideal candidate must be able to work on team research themes and supervise graduate students in an open environment where collaborations with experts in other fields at CRIM is valued.
 
The position is offered on a one-year basis with the possibility of renewal for up to 3 or 4 years depending on performance and funding.
 
Responsibilities:

● Perform high quality research on speaker recognition and anti-spoofing

● Assist in supervising graduate students

● Publish in referred journals and conferences

● Write/contribute to grant applications for new research projects

SKILLS AND EXPERIENCE
Required qualifications: 

● Doctoral degree (Ph.D.) in a relevant field

● Exceptional academic record and a clear aptitude for research ● Experience in student supervision ● Good publication record

● Excellent verbal and written communication skills in English
 
 
Preferred qualifications:

● Familiarity with modern distributed programming environments and with languages such as C++, Python and Perl

● Programming experience with relevant tools such as Theano, TensorFlow, Torch or Kaldi

● Proficiency in written and spoken French 
 
 
ABOUT THE ENVIRONMENT 
Located in Montreal, an historical, vibrant and culturally diverse city with 6 universities, recognized for its safety and low cost of living. Already a favorite of high tech and creative industries, Montreal have recently received large public and private investments and been turned into a leading center in machine learning and artificial intelligence research. 

GOOD REASONS FOR JOINING CRIM’S TEAM
● Benefit from various attractive employment conditions (Drug and health insurance plan, Pension plan, Competitive salary, French training programs)

● Reap the benefits of an outstanding work atmosphere, characterized by mutual support and good humour 

● Work alongside passionate people in a collaborative setting 

● Maintain work/family balance and quality of life 

HOW TO APPLY
Apply directly to emploi@crim.ca, or use our online form.
 
CRIM is an equal opportunity employer and values diversity. We encourage the development of ideas as a team and cultivate an open work environment that respects differences. We encourage all candidates to apply for this position; however, only selected individuals will be contacted. Thank you for your interest in CRIM!
 
Join CRIM’s team and work with dynamic and passionate people!

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Professor (W2) Speech Technology and Hearing Devices at University of
Oldenburg, Cluster of Excellence Hearing4all
Oldenburg, Germany (website: http://hearing4all.eu/EN/)

-> link: https://uol.de/stellen/?stelle=66194

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Title: PhD positions at the University of Edinburgh

Link: http://www.edinburghnlp.org/cdt/

Text:

FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

School of Informatics
School of Philosophy, Psychology and Language Sciences
University of Edinburgh

UK Research and Innovation has recently announced funding for a Centre
in Doctoral Training in Natural Language Processing (CDT in NLP) at
the University of Edinburgh. This CDT offers unique, tailored doctoral
training consisting of both taught courses and a doctoral
dissertation. Both components run concurrently over four years. Each
student will take a set of courses designed to complement their existing
expertise and give them an interdisciplinary perspective on NLP. They
will received full funding for four years, plus generous
funding for travel, equipment, and research costs.

The CDT brings together researchers in NLP, speech, linguistics, cognitive
science, and design informatics from across the University of Edinburgh.
Students will be supervised by a team of over 40 world-class faculty
and will benefit from cutting edge computing and experimental facilities,
including a large GPU cluster and eye-tracking, speech, virtual reality,
and visualization labs. The CDT involves over 20 industrial partners,
including Amazon, Facebook, Huawei, Microsoft, Mozilla, Reuters, Toshiba,
and the BBC. Close links also exist with the Alan Turing Institute and the
Bayes Centre.

The first cohort of CDT students will start in September 2019, and we
are now seeking applications. A wide range of research topics fall
within the remit of the CDT:

• Natural language processing and computational linguistics

• Speech technology

• Dialogue, multimodal interaction, language and vision

• Information retrieval and visualisation, computational social science

• Computational models of human cognition and behaviour, including
  language and speech processing

• Human-Computer interaction, design informatics, assistive and
  educational technology

• Psycholinguistics, language acquisition, language evolution,
  language variation and change

• Linguistic foundations of language and speech processing
  Approximately 8 studentships are available, covering both maintenance
  at the research council rate of GBP 15,009 per year and tuition fees.
  Studentships are available for UK, EU, and non-EU nationals.

Applicants should have an undergraduate or master?s degree in computer
science, linguistics, cognitive science, AI, or a related discipline. We
particularly encourage applications from women, minorities, and members
of other groups that are underrepresented in technology.

Further details including the application procedure can be found at:

http://www.edinburghnlp.org/cdt/

In order to ensure full consideration for funding, applications
(including all supporting documents) need to be received by 29 March
2019. Please direct inquiries to the PhD admissions team at
cdt-nlp-admissions@inf.ed.ac.uk.

The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Postdoctoral researcher

Job description
CONTEXT IRISA (https://www.irisa.fr/) is the largest research laboratory dedicated to computer science in France, hosting more than 800 people and 40 research teams. Its activities spans all the fields of computer science. It is located in Rennes, Lannion, and Vannes. The Expression team (https://www-expression.irisa.fr/) focuses on natural language processing (NLP), be it through texts, speech or gestures. In particular, it has internationally recognized expertise in textto-speech (TTS). The opened position is part of a project aimed at the synthesis of the Breton language. The recruited person will collaborate with other researchers and engineers implied in NLP and TTS.
TASKS Development of NLP modules for Breton and integration in a TTS pipeline, i.e.:

1. Phonetization, grapheme-to-phoneme conversion.

2. Text normalization.

3. POS tagging and chunking ;

4. Adaptation of the team’s TTS engine. The position also includes data management and project monitoring tasks.
REQUIRED QUALIFICATION & SKILLS

• PhD in computer science

• Recent skills in natural language processing

• Recent skills in machine learning

• Top academic and publication records

• Good communication skills

• Team work experience

CONTRACT

• 18 month, full time.

• Campus of Lannion (22).

• Competitive salary, depending on the experience.

• Starting as soon as possible.

APPLICATION & INFORMATION

Please send a cover letter and resume to :

• Gwénolé Lecorvé, gwenole.lecorve@irisa.fr

• Damien Lolive, damien.lolive@irisa.fr

Application deadline: 31th March 2019.

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Two PhD positions in the area of Information Extraction, Data Mining and Machine Learning at Saarland University

We anticipate the availability of funds for two PhD positions in the area of information extraction, data mining and machine learning.

The project aims at designing a framework for extracting evidence and actionable intelligence from large amount of noisy multilingual multimodal data based on advanced speech and language technologies (SLTs), visual analysis (VA) and network analysis (NA). The overall project goal is to achieve a significant improvement in identification of events, entities and relations, and to design a new generation of probabilistic and neural networks based tools interfacing SLT, VA and NA technologies. The research will be carried out together with a European consortium of high-profile research institutes and companies.

The successful candidate should have a degree in computer science, computational linguistics, mathematics, physics or a discipline with a related background. Excellent programming skills in modern object-oriented languages are required, as well as strong analytical and problem-solving skills. Relevant expertise in the area of the project is desired. Very good oral and written communication skills in English are required.
This work will be conducted at the Spoken Language Systems group (http://www.lsv.uni-saarland.de/) at Saarland University.
Saarland University (http://www.uni-saarland.de/en/) is a European leader in Computer Science research and teaching, and is particularly well-known for its research in Computational Linguistics and Natural Language Processing. In addition, the Max Planck Institute for Computer Science, the Max Planck Institute for Software Systems and the German Research Center for Artificial Intelligence (DFKI) are located on campus. Students and researchers come from many countries and the research language is English.

The department of Language Science and Technology at Saarland University 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. It also runs a significant number of European and nationally funded projects.
Both positions are 3-years positions with a salary German TV-L E13 scale (75%). The starting salary is about 34,000 euros per year and increases with experience.  
Each application should include:

* Curriculum Vitae including a list of publications (if applicable)
* Transcript of records
* Short statement of interest (not more than half a page)
* Names of two references
* Any other supporting information or documents

Applications (documents in PDF format in a single file) should be sent to: Dietrich.Klakow@lsv.uni-saarland.de
Priority will be given to applications received by Monday April 15th 2019.
Further inquiries regarding the project should be directed to: Michael A. Hedderich or Olga Petukhova

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L'équipe Analyse-Synthèse de l'IRCAM recherche un/une chargé(e) de recherche pour le
développement des technologies relatives à l?analyse automatique d?enregistrements
musicaux (progression d?accords, identification d?instruments, qualité audio,
auto-tagging).

EXPÉRIENCE ET COMPÉTENCES REQUISES :

- Titulaire d?un doctorat, le/la candidat/e a une très bonne connaissance du traitement
du signal (analyse spectrale, extraction de descripteurs audio, estimation de paramètres)
ainsi qu?en algorithmes d?apprentissage automatique (SVM, ConvNet) et en calcul distribué
;
- Il/Elle maîtrise la programmation Matlab, Python et C/C++ ;
- Il/Elle a une bonne connaissance de l'environnement UNIX (GNU-Linux ou MacOSX) ;
- Haute productivité, travail méthodique, excellent style de programmation, bonne
communication, rigueur.

Toutes autres information sont disponibles à cette adresse :
  https://www.ircam.fr/job-offer/chargee-de-recherche-3/

Prière d'envoyer une lettre de motivation avec la référence 201903UMGRES et un CV
détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi
que toute autre information pertinente) à mignot at ircam dot fr avec copie à vinet at
ircam dot fr et roebel at ircam dot fr.

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-- ************************ Professor Phil Green SPandH Dept of Computer Science University of Sheffield ************************ 

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Senior Speech Processing Engineer (M/F) 

CONTEXT

Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of developing the speech recognition architecture on our new platform, managing the expertise topics related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.

MISSIONS

Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:

• Define, specify and implement the speech recognition architecture taking into account the limits and interests of both modes (embedded/deported)
• Prototype and model the different dialogue platforms
• Write the technical specifications of these HMIs
• Study, analyze and respond to calls for tenders related to the subject
• Participate in customer and supplier workshops
• Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
• Identify and document speech recognition technological innovations to extract added value for the company
• Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
• Collaborate within a dynamic and motivated team
• Ensure that speech recognition works properly on the new platform
• Create dialogue platforms that combine flexibility of use, robustness and performance
• Provide expertise and support on the theme 'Speech Processing': advice, methods, tools

PROFILE

You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.

You have at least 5 years of experience in the field of speech recognition. 

A good command of C++ and Python languages and a solid knowledge of speech processing are essential.

Ideally, you are familiar with:

• Understanding natural language
• Signal processing
• Nuance Technologies
• Google Assistant
• Alexa 
• Java development under Android 

Occasional travel is expected.

Fluent French is required.

Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.

Skills in Chinese would be a plus.

To apply you can send your resume directly to this email : mathilde.tarente@faurecia.com  or go to our website and apply by clicking on the link under the corresponding job offer : https://parrot-faurecia-automotive.talentview.io/jobs/7lz0hr?source=site_entreprise

We will get back to you shortly !!

• Annonce pour le poste de Voice Assistant Integration Engineer (M/F) :

Would you like to be part of a French company known for its innovation and agility? 

Come and join Parrot Faurecia Automotive, a leading automotive equipment manufacturer with a start-up mentality, working to develop the cockpit of the future!

Located in central Paris, our teams engineer innovative technologies by developing man-machine interfaces based on Android Auto to provide an intuitive connected experience to end users. Our embedded electronic solutions are equipped with powerful multi-core graphics processors to provide quick response times for multiple simultaneous instructions.

Join us in taking up one of the greatest challenges of the automotive industry!

We are looking for a Voice Assistant Integration Engineer (M/F) 

CONTEXT

Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of managing the expertise subjects related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.

MISSIONS

Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:

• Prototype and model the different dialogue platforms
• Write the technical specifications of these HMIs
• Study, analyze and respond to calls for tenders related to the subject
• Participate in customer and supplier workshops
• Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
• Identify and document speech recognition technological innovations to extract added value for the company
• Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
• Collaborate within a dynamic and motivated team

PROFILE

You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.

You have at least 2 years of experience in the field of speech recognition.

You are proficient in C++ language, Java development under Android and you have a solid knowledge of Google and Alexa assistants.

Ideally, you are familiar with:

• Understanding natural language
• Speech processing
• Signal processing
• Nuance Technologies
• The Python language

Occasional travel is expected.

Fluent French is required.

Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.

Skills in Chinese would be a plus.

To apply you can send your resume directly to this email : mathilde.tarente@faurecia.com  or go to our website and apply by clicking on the link under the corresponding job offer : https://parrot-faurecia-automotive.talentview.io/jobs/zxjk2o?source=site_entreprise

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The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Project Manager Intellectual Property and Language Data position.

Under the CEO?s supervision, the Project Manager Intellectual Property and Language Data will handle legal issues related to compilation, use and distribution of language datasets on a European and international scale. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field.

His main tasks will consist of:

• drafting and negotiating distribution contracts for language datasets to be added to an online catalogue;
• analysing of the legal status of language datasets;
• implementing GDPR requirements in the processing of language data;
• supervising data collection, particularly in the context of public Open Data;
• implementing evaluation procedures for IPR clearance of digital data.

A successful candidate:

• holds a Master?s degree (or equivalent) in IT Law, with good understanding of intellectual property and data protection;
• holds a Bachelor?s degree (or justifies equivalent experience) in Information Science, Knowledge Management or a similar domain;
• speaks fluent English, with advanced writing and analytical skills;
• is familiar with public licensing schemes (CC, GPL, etc.);
• justifies experience in project management and/or participation in European or international projects;
• is dynamic, communicative, flexible and willing to work on various tasks;
• is capable of working independently as well as in a team;
• is an EU citizen, or has a residence permit enabling him to work in France.

All applications will be carefully examined until the position is filled. The position is based in Paris.

Gross annual salary: 30.000-36.000 EUR depending on experience.

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.

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

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Post-doc/PhD position Pattern mining for Neural Networks debugging: application to speech recognition

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PhD Title: Remote and Automatic Monitoring of Bird Populations

Studentship: Full Scholarship, including fees (EU/Non EU) plus annual stipend of €18,000.

Start Date: Sept 2019

PhD Supervisor: Dr. Naomi Harte, Sigmedia Group, Electronic & Electrical Engineering, Trinity College Dublin, Ireland

Background:

The analysis of birdsong has increased in the speech processing community in the past 5 years. Much of the reported research has concentrated on the identification of bird species from their songs or calls. Birdlife International has identified over 12,000 sites globally that are home to bird species of conservation concern and other forms of biodiversity. Out of these, 422 are in danger due to a number of threats including human encroachment and climate change. One of the main challenges in properly managing these sites is adequately monitoring them to determine their state, threats to the ecosystem and responses to these threats. Remote monitoring is the best potential option to achieve the level of coverage required.

The objective of this PhD project is to define the next-generation approaches to the use of remote monitoring for populations of birds of conservation concern. This PhD programme will develop acoustic techniques for the monitoring of bird species of conservation concern by leveraging recent developments in speech and language processing technologies. The PhD will develop appropriate approaches to acoustic data collection in the wild to ensure that acoustic surveys yield accurate bird population data and investigate audio signal analysis steps necessary to extract useful information from these long recordings. In particular the student will focus on signal enhancement to mitigate noise, and the idea of diarisation, i.e. the concept of 'who sang when'. This ambitious approach will take concepts from speaker diarisation in the speech processing domain and attempt to make sense of recordings overall. Birdsong presents significant challenges beyond speech, with more rapid pitch fluctuations coupled with noisier recordings in the wild. Thus the research is very far from a re-application of knowledge from one domain to another. Also, rather than trying to identify specific species in a recording from a closed set of possible birds, this approach will consider an unconstrained set to add to the technical challenges and make the results even more impactful. The desire is to exploit online archives of birdsong recordings from experts such as those available on xeno-canto.org and The Macaulay Library at Cornell. Based on the known geographical location of a recording, spontaneous models of bird vocalisations from populations in that area could be constructed using machine learning performed on available archived recordings. Techniques developed originally for speaker

identification will be further developed for this application. This work will also leverage deep learning to quickly build accurate models from these large datasets.

Envisaged Outputs of the Research:

• • Signal processing algorithms to address noise issues specific to remote recordings in bird habitats.
• • Exploitation of advanced machine learning approaches, including deep learning, to identify portions of recordings that contain bird activity.
• • Disruptive approaches to automatic bird species identification to leverage opensource repositories to identify birds present in the recordings.

Requirements:

The ideal candidate for this position will:

• • Have a primary degree (first class honours) in Electrical Engineering, Computer Engineering or a closely related discipline.
• • Possess strong written and oral communication skills in English.
• • Have a strong background and interest in digital signal processing (DSP)
• • Have strong coding skills
• • Be mathematically minded, and be curious about nature.

Interested candidates should send an email to Prof. Naomi Harte at nharte@tcd.ie. The email MUST include the following:

• • Candidate CV (max 2 pages)
• • A short statement of motivation (half page)
• • Scanned academic transcripts
• • Proof of English language competency (where applicable, see 1)
• • Name and contact details for TWO academic referees

1 https://www.tcd.ie/study/international/how-to-apply/entry-requirements.php#language-requirements

Incomplete applications may not be considered.

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Two PhD Positions in Computational Linguistics or Phonetics or Speech Science

We are pleased to announce the availability of two PhD positions in the Language Science
and Technology department at Saarland University in a project funded by the German
Research Foundation (DFG). The three-year project is entitled 'Pause-internal phonetic
particles' and is directed by Jürgen Trouvain and Bernd Möbius. Its focus is on the
production and perception of vocalisations such as breath noises and tongue clicks
typically found in speech pauses. Research in the project will be based on corpus
analyses and production and perception experiments and develop pause models for speech
synthesis.

The successful candidates should have a Master's degree in Computational Linguistics or
Phonetics or Speech Science, or a related discipline. A good command of English is
mandatory. Working knowledge of German is desirable but not a prerequisite. Candidates
must have completed their Master studies by the time of the appointment. We are happy to
consider applicants who have not yet finished their MA/MSc by the time of application but
will have submitted their thesis by the starting date. Both contracts are funded for
three years at a 65% salary on the German TV-L 13 scale.

The doctoral researchers will join a vibrant community of speech and language researchers
at Saarland University whose expertise spans areas such as computational linguistics,
psycho-linguistics, language and speech technology, speech science, theoretical and
corpus linguistics, computer science, and psychology. Saarland University offers a lively
academic environment for phonetics research. The department of Language Science and
Technology is one of the leading departments in speech and language in Europe, with
approximately 50 postdoctoral researchers and PhD students. The flagship project at the
moment is the Collaborative Research Centre on Information Density and Linguistic
Encoding. It also runs a significant number of European and nationally funded projects.

Applicants with a degree in Phonetics, Computational Linguistics, Spoken Language
Processing, Speech Technology, or related fields, are encouraged to submit a full CV
(including transcripts and copies of certificates, as well as two references) and a
sample of written academic work, accompanied by a cover letter, to
trouvain[at]coli.uni-saarland.de and moebius[at]coli.uni-saarland.de. Review of
applications will begin on June 3, 2019, and will continue until the suitable candidates
are found.

Dr. Jürgen Trouvain and Prof. Bernd Möbius

--
Jürgen Trouvain
Saarland University
Language Science and Technology
Campus C7.2
D-66123 Saarbrücken
Tel.: +49 - (0)681 - 302 - 46 94

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The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Technical Engineer/Scientist (Project Manager) position, specialized in Speech and Multimodal technologies.

Technical Engineer / Scientist (Project Manager) in Speech and Multimodal Technologies

Under the supervision of the CEO, the responsibilities of the Technical Engineer/Scientist include designing/specifying language resources, setting up production frameworks and platforms, carrying out quality control and assessment. He/she will be in charge of renovating the current language resources production workflows. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field. He/she will be in charge of conducting the activities related to language resources and Speech and Multimodal technologies. The task will mostly consist in managing language resources production projects and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the development team.

Profile :

• PhD in computer science, speech, audiovisual/multimodal technologies
• Experience and/or good knowledge in speech data collection, expertise in phonetics, transcription tools
• Experience in speech recognition, synthesis, speaker ID and the well-used packages (e.g. KALDI) and the tools to produce, collect and assess quality of resources and datasets
• Experience and/or good knowledge of the Language Technology area
• Experience with technology transfer projects, industrial projects, collaborative projects within the European Commission or other international frameworks
• Good knowledge of Linux and open source software
• Proficiency in Python
• Hands-on experience in Django is a plus
• Ability to work independently and as part of a team, in particular the ability to supervise members of a multidisciplinary team
• Dynamic and communicative, flexible to combine and work on different tasks
• Proficiency in French and English
• Citizenship of (or residency papers) a European Union country

All applications will be carefully examined until the position is filled. The position is based in Paris.

Salary: Commensurate with qualifications and experience.

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.

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

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Dans le cadre du projet national ANR VocADom, le Laboratoire d?Informatique de Grenoble
(LIG) recrute un ingénieur Développeur Domotique/Traitement multimédia temps-réel.
L?objectif général du projet de recherche industrielle VocADom vise à définir, en lien
avec les utilisateurs finaux, les fonctionnalités d?un système domotique à commande
vocale s?adaptant à l?utilisateur et utilisable au domicile dans des conditions réelles
(bruit, présence de plusieurs personnes). Plus détails peuvent être trouvé sur le site du
projet VocADom (https://vocadom.imag.fr).

*Mission :* La mission consiste à adapter le middleware OpenHAB gérant les capteurs et
les actionneurs de l?appartement intelligent du LIG et d?un mini-kit domotique portable
aux spécifications du projet et à réaliser l?interfaçage avec les outils de traitements
automatiques développés par les partenaires du projet. Par ailleurs, l?ingénieur sera
chargé de la mise en place technique des expérimentations et de leurs scénarios, incluant
la production et la synchronisation des données multimédia (vidéo, audio, traces
domotiques). Enfin, il sera soutien de l?intégration des algorithmes temps-réel des
équipes de recherche dans l?architecture temps-réel développée par l?entreprise THEORIS,
partenaire du projet. Le travail proposé comprendra les tâches suivantes :
* prise en main et adaptation de l?infrastructure domotique OpenHab de l?appartement
intelligent du LIG ;
* conception du mini-réseau domotique portable (sur la base d?une architecture existante)
;
* soutien aux expérimentations du projet ;
* soutien à l?intégration des algorithmes temps-réels des équipes de recherche ;
* documentation.

*Profil et compétences souhaitées :*

* niveau ingénieur ou M2 en informatique,
* compétences opérationnelles en génie logiciel (gestion de version, tests, qualité de
code),
* connaissance de Java, OSGi, C, C++, Linux,
* autonomie et force de proposition, capacité de gestion de projet,
* une première expérience d?utilisation des middlewares domotiques (OpenHAB, UPnP,
KNX...) serait un plus.

*Salaire :* 1650 à 1950e net/mois selon l?expérience

*Environnement de travail :* Le poste sera rattaché au Laboratoire d?Informatique de
Grenoble, UMR CNRS, au sein de l?équipe GETALP. L?équipe GETALP (http://getalp.imag.fr/)
regroupe plus de 40 chercheurs, ingénieurs et étudiants dans le domaine du traitement
automatisé des langues et de la parole multilingue. Le candidat sera également amené à
collaborer étroitement avec l?équipe IIHM du LIG et avec les collaborateurs de la Maison
de l?Innovation et de la Création (Maci).

*Candidature* Envoyer un CV, une lettre de motivation accompagnée éventuellement de 1 à 3
lettres de recommandation à Michel.Vacher@imag.fr et Francois.Portet@imag.fr. Les
candidatures seront examinées dès à présent au fil de l?eau jusqu?à la date de démarrage.
Merci de candidater dès que possible avant cette date.

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2 PhDs in Trinity College Dublin, Ireland, to start in Sept 2019. Both come with a stipend of 18000Euros per year, along with full student fees for a 4 year period. Please contact me  at nharte@tcd.ie if interested in either post.

Human Speech?  How do I know it’s Real?

20 years ago, the major focus in developing speech synthesis systems was testing the intelligibility of the output speech. More recently, attention has switched focus to assessing not only intelligibility, but also naturalness, pleasantness, pauses, stress, intonation, emotion and listening effort. The intelligibility of systems is now so high, that synthetic voices are becoming more human-like. This is good news for generating realistic synthetic speech for applications such as voice reconstruction or gaming. In tandem, research in the area of speaker verification, or voice based biometrics, has started to pay closer attention to the issue of spoofing – where systems are attacked with reconstructed speech. Now, with improvements in speech synthesis, another realistic form of spoofing is the use synthetic speech generated by modelling the target user. So how can you tell when speech is real, or when it is fake? This is the focus of this PhD project and it goes to the very core of the nature of human speech.

Remote and Automatic Monitoring of Bird Populations

The objective of this PhD project is to define the next-generation approaches to the use of remote monitoring for populations of birds of conservation concern. This PhD programme will develop acoustic techniques for the monitoring of bird species of conservation concern by leveraging recent developments in speech and language processing technologies. The PhD will develop appropriate approaches to acoustic data collection in the wild to ensure that acoustic surveys yield accurate bird population data and investigate audio signal analysis steps necessary to extract useful information from these long recordings. Approaches will involve the use of signal processing and deep learning. The research will be conducted in collaboration with the Dept of Zoology at TCD.

--

Associate Professor Naomi Harte

School of Engineering,

Trinity College Dublin

www.sigmedia.tv

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The Department of Speech-Language Pathology, in the Faculty of Medicine at the University of Toronto, invites applications for a full-time tenure-stream appointment in the field of pediatric language disorders. The appointment will be at the rank of Assistant Professor and will commence on January 1, 2020 or shortly thereafter.

The successful candidate must have a Ph.D. in Speech-Language Pathology, Communication Disorders or a related field of research and expertise in child language and child language disorders, with a focus on semantics, syntax, pragmatics or literacy with a demonstrated record of excellence in research and teaching. Completion of a post-doctoral fellowship will be considered an asset, as will experience supervising student research and/or post-doctoral fellows. We require that candidates have a clinical background in speech-language pathology/communication disorders and are eligible for registration with the College of Audiologists and Speech-Language Pathologists of Ontario (CASLPO). We seek candidates whose research and teaching interests complement and strengthen our existing departmental strengths. The successful candidate will be expected to pursue innovative and independent research at the highest international level and to establish an outstanding, competitive and externally funded research program.

Research excellence should be demonstrated by high-quality peer-reviewed publications, peer-reviewed funding, the submitted research statement, presentations at significant conferences, awards and accolades, and strong endorsements from referees of high standing.

Evidence of excellence in teaching will be provided through teaching accomplishments and awards, the teaching dossier, including a detailed teaching statement, sample course syllabi, and teaching evaluations submitted as part of the application, as well as strong letters of reference. 

Salary will be commensurate with qualifications and experience. 

This is an exceptional opportunity to join the Department of Speech-Language Pathology at the University of Toronto, one of the most highly ranked research universities in North America. The department is housed in the Rehabilitation Sciences Building, which provides excellent teaching and research facilities. The University of Toronto offers unique opportunities for collaborative and interdisciplinary research, encourages innovative scholarship, and provides the prospect of teaching a diverse student population. 

All qualified candidates are invited to apply by clicking the link below. Applicants must submit a cover letter, curriculum vitae, research statement (up to 3 pages), copies of up to three representative publications and a teaching dossier to include a detailed teaching statement (up to 3 pages), sample syllabi, and teaching evaluations.

Applicants must also arrange to have three letters of reference sent directly by the referee via email (on letterhead and signed) to search.slp@utoronto.ca by the closing date. If you have questions about this position, please contact search.slp@utoronto.ca. All application materials must be submitted online.


Submission guidelines can be found at http://uoft.me/how-to-apply. We recommend combining attached documents into one or two files in PDF format. 

All application materials, including reference letters, must be received by July 18, 2019.

For more information about the Department of Speech-Language Pathology, please visit our home page at https://slp.utoronto.ca/. 

The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ persons and others who may contribute to the further diversification of ideas. 

As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

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Post-doc/PhD position Pattern mining for Neural Networks debugging: application to speech recognition

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SUBJECT TITLE: Bio-Bayes Predictions -- Coupling Biological and Bayesian Predictive
Models in Neurocognitive Speech Processing
RESEARCH FIELD: Computer science, Cognitive Science, Psychological sciences, Neurosciences
SCIENTIFIC DEPARTMENT (LABORATORY?S NAME): LPNC -- Laboratoire de Psychologie et
NeuroCognition
DOCTORAL SCHOOL?S: MSTII -- Mathématiques, Sciences et technologies de l'information,
Informatique SUPERVISOR?S NAME: Julien Diard, PhD

SUBJECT DESCRIPTION:
The issue of the relationship between perception and production mechanisms is central to
many domains in cognitive science. In speech communication for instance, where
predictions from speech production simulation interact in various ways with perceptual
processes. In this context, we have developed COSMO (Communicating about Objects using
SensoriMotor Operations), a family of Bayesian algorithmic models of communicating
agents. We have previously used such models to study the evolution of phonological
systems (Moulin-Frier et al., 2015), speech perception and learning (Laurent et al.,
2017; Barnaud et al., 2017; 2018), and speech production and adaptation (Patri et al.,
2015; 2018).

However, so far, these models consider greatly simplified temporal dimensions. For
instance, syllable perception was restricted to consonant-vowel syllables, assuming that
the key points of speech trajectories, respectively at the center of the consonant and
the vowel, were previously identified. This, of course, contrasts with natural speech
processing, where sensory inputs and motor controls continuously unfold over time.
Indeed, the neuronal substrates, in the brain, that deal with auditory input are well
described in terms of their oscillatory nature, since they intrinsically have to deal
with temporal properties of speech, and their predictive nature, since they aim at
anticipating events.

In this PhD project, we aim at extending the COSMO framework to define the first Bayesian
perceptuo-motor model of continuous speech communication. In previous work, in the domain
of Bayesian visual word recognition modeling (Phénix et al., 2018; Ginestet et al.,
2019), we have developed mathematical tools to describe the temporal dynamics of
perceptual evidence accumulation across layers of hierarchical representations.
Probability distributions at each layer (letters and words) evolve continuously over
time, as a function of bottom-up sensory evidence and top-down lexical constraints, to
predict upcoming events. Crucially, we have developed mathematical tools to model, on the
one hand, attentional control of these information flows, and, on the other hand,
asynchronous and asymmetric information transfer. Applying these mathematical constructs
to speech communication modeling would yield a novel class of Bayesian hierarchical and
predictive models, able to account for observations of neuronal oscillatory systems in
the brain.

This PhD project is part of an international collaboration with Anne-Lise Giraud and
Itsaso Olasagasti of the ?Auditory, Speech and Language Neuroscience? group at UNIGE
(Université de Genève, Switzerland), with regular meetings and visits to Geneva planned.
This international collaboration with Geneva will provide a unique framework for mixing
the Bayesian approach with neuroscience constraints and data, providing a valuable
multidisciplinary environment for the PhD project. The PhD candidate will integrate the
?Language? research team at LPNC (Laboratoire de Psychologie et NeuroCognition,
Grenoble), will be supervised by Julien Diard (CR CNRS, LPNC) and Jean-Luc Schwartz (DR
CNRS, Gipsa- Lab), and will be registered in the MSTII (Mathématiques, Sciences et
technologies de l'information, Informatique) doctoral school of Univ. Grenoble Alpes.

ELIGIBILITY CRITERIA
Applicants must hold a Master's degree (or be about to earn one) or have a university
degree equivalent to a European Master's (5-year duration),
Applicants will have to send an application letter in English and attach: - their last
diploma;
- their CV;
- letters of recommendation are welcome.
Address to send their application: Julien.Diard@univ-grenoble-alpes.fr

SELECTION PROCESS
Application deadline: June, 21, 2019 at 17:00 (CET) Applications will be evaluated
through a three-step process:
1. Eligibility check of applications before June, 24, 2019
2. 1st round of selection: the applications will be evaluated by a Review Board between
June, 24, and June, 28,
2019. Results will be given June, 28, 2019.
3. 2nd round of selection: shortlisted candidates will be invited for an interview
session in Grenoble on July, 5,
2019 (in Grenoble or by Skype).

TYPE of CONTRACT: temporary-3 years of doctoral contract JOB STATUS: Full time
HOURS PER WEEK: 35
OFFER STARTING DATE: October, 1st, 2019
APPLICATION DEADLINE: June, 21, 2019
Salary: between 1768.55 ? and 2100 ? brut per month (depending on complementary activity
or not)
PROFILE SUMMARY:
The candidate must have a background in computer science, applied mathematics or signal
processing, with a strong affinity for cognitive sciences, or instead a background in
cognitive science or computational neuroscience, with a strong affinity for mathematical
modelling. This training must be validated, or be validated soon (before Summer 2019), by
a Masters level diploma (or equivalent).

The candidate must have previous experience in scientific research in a laboratory.
Mastery of a scientific calculus language, or of a general-purpose programming language
is required (R, Python, Matlab, etc.). The candidate must also have a good command of
scientific English, both spoken and written.

Previous experience in probabilistic modeling, dynamic systems modeling, or connectionist
modeling are a plus but are not required. Previous knowledge of the field of perception
modeling or speech production is also a plus.

 

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Lieu de travail : Grenoble

Date de publication : 1 Mai 2019

Noms des responsables scientifiques : Gérard Bailly (DR CNRS, GIPSA-Lab, U. Grenoble Alpes) et Pascal Huguet (DR CNRS, LAPSCO, U. Clermont Auvergne)

Type de contrat : CDD Doctorant/Contrat doctoral

Durée du contrat : 36 mois

Date de début de la thèse : 1 octobre 2019

Quotité de travail : Temps complet

Rémunération : 2 135,00 € brut mensuel

Sujet de la these:

Impact de la présence de robots attentifs et bienveillants sur les comportements et fonctionnements cognitifs humains

Contexte

La robotique sociale humanoïde vise à créer des robots capables d’interagir avec des partenaires humains à des fins de coopération dans des secteurs aussi variés que l’assistance aux personnes âgées, l’apprentissage de compétences par les enfants ou encore la cobotique dans l’industrie 4.0. L’objectif est souvent de substituer à un partenaire humain ou animal le robot dans des tâches où les propriétés d’endurance, de rapidité ou de flexibilité des attributions sociales de ces avatars sociaux sont bénéfiques. Si beaucoup d’études en interaction face-à-face montrent l’impact des comportements verbaux [1] et coverbaux [2] des robots sociaux sur ceux de leurs partenaires humains, peu d’études ont été consacrées aux effets comparés entre présence robotique vs. humaine en matière de surveillance de tâches dans lesquelles le robot ou son modèle humain ne sont pas directement engagés. Deux études récentes conduites au LAPSCO [3] [4] ont notamment permis de répliquer avec succès, sous condition de présence robotique, l’influence généralement bénéfique de la présence humaine en matière de contrôle de l’attention dans des tâches impliquant de réprimer un automatisme cognitif néfaste dans l’activité cible (e.g. [5]).

Sujet et plan de travail

Nous proposons ici d’explorer l’impact du comportement d’un robot humanoïde possédant des capacités de communication verbale et co-verbale étendues [6] sur les performances (en matière de contrôle cognitif) de sujets impliqués dans des tâches dans lesquelles le robot est directement impliqué ou plus simplement présent dans l’environnement d’interaction. Nous allons donc varier le degré avec lequel le robot s’immisce dans la tâche principale, afin de déterminer les signaux susceptibles d’optimiser l’expression des bénéfices attachés aux robots sociaux humanoïdes (et à terme, leur acceptabilité) et les signaux plus néfastes à éliminer.

Dans le cadre de cette thèse, nous explorerons l’impact du comportement robotique – dans un premier temps, téléopéré par un pilote humain [7] puis piloté par les modèles appris sur ces données comportementales [8] – sur le comportement observable des sujets (e.g., performances cognitives, signaux verbaux et co-verbaux, notamment regard), sur leurs signaux physiologiques (i.e. conduction dermale, rythme respiratoire et cardiaque) et sur leurs activités cérébrales (i.e. étude de l’onde négative d’erreur par EEG).

Retombées scientifiques et technologiques

Les retombées attendues de cette thèse sont multiples : d’abord des modèles de prédiction des liens entre comportements observables, variables physiologiques et activités cérébrales sous-jacentes, ceci en fonction des performances observées et des comportements actifs et réactifs du robot ; ensuite, un ensemble de stratégies de comportement de robots attentifs et bienveillants, adaptées aux profils psychologiques des sujets et aux indicateurs de performance propres à la tâche ; et enfin des protocoles d’évaluation automatique de ces profils psychologiques par des robots sociaux interactifs, susceptibles d’être déployés dans des applications en santé, éducation ou évaluation de ressources humaines.

Références

[1] K.-Y. Chin, Z.-W. Hong, and Y.-L. Chen, “Impact of using an educational robot-based learning system on students’ motivation in elementary education,” IEEE Transactions on learning technologies, vol. 7, no. 4, pp. 333–345, 2014.

[2] S. Andrist, X. Z. Tan, M. Gleicher, and B. Mutlu, “Conversational gaze aversion for humanlike robots,” presented at the Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 2014, pp. 25–32.

[3] N. Spatola et al., “Improved Cognitive Control in Presence of Anthropomorphized Robots,” International Journal of Social Robotics, pp. 1–14, 2019.

[4] N. Spatola et al., “Not as bad as it seems: When the presence of a threatening humanoid robot improves human performance,” Science Robotics, vol. 3, no. 21, eaat5843, 2018.

[5] D. Sharma, R. Booth, R. Brown, and P. Huguet, “Exploring the temporal dynamics of social facilitation in the Stroop task,” Psychonomic bulletin & review, vol. 17, no. 1, pp. 52–58, 2010.

[6] Parmiggiani, Alberto, Elisei, Frédéric, Maggiali, Marco, Randazzo, Marco, Bailly, Gérard, and Metta, Giorgio, “Design and validation of a talking face for the iCub,” International Journal of Humanoid Robotics, vol. 12, no. 3, 20 pages, 2015.

[7] R. Cambuzat, F. Elisei, G. Bailly, O. Simonin, and A. Spalanzani, “Immersive teleoperation of the eye gaze of social robots,” in Int. Symposium on Robotics (ISR), Munich, Germany, 2018, pp. 232–239.

[8] Nguyen, Duc-Canh, Bailly, Gérard, and Elisei, Frédéric, “Learning off-line vs. on-line models of interactive multimodal behaviors with Recurrent Neural Networks,” Pattern Recognition Letters (PRL), pp. 29–36, 2017.

Au sein du département Parole & Cognition du GIPSA-Lab (Grenoble, France), l'équipe Cognitive Robotics, Interactive Systems & Speech Processing (CRISSP) développe des modèles de comportements multimodaux (parole, gestes, regard, etc.) pour des robots humanoïdes en interaction avec des partenaires humains. Elle s'appuie sur les moyens techniques du GIPSA-Lab et en particulier sur la plate-forme robotique MICAL qui gère le robot iCub NINA et les outils de développement.

Cette thèse s'inscrit dans le projet de l'équipe de développer des robots engagés dans des tâches finalisées nécessitant un contrôle fin de l’engagement. Elle est financée par le projet 80 ans du CNRS «Robotique Sociale Humanoïde et Cognition » (RSHC) impliquant des chercheurs de trois laboratoires : le LAPSCO et le LIMOS à Clermont et le GIPSA-Lab à Grenoble,

La thèse sera rattachée à l’école doctorale EEATS de Grenoble. Des déplacements et trois séjours de courte-durée à Clermont-Ferrand seront à prévoir pour collaborer avec les chercheurs du LAPSCO et du LIMOS de l’UCA.

Le candidat devra être titulaire d’un diplôme d’ingénieur et/ou d’un master en Sciences Cognitives ou en Robotique d’interaction. Le poste nécessite de solides connaissances en expérimentation, programmation et analyse statistique. Une formation initiale en neurosciences est un plus. Le candidat doit avoir de bonnes aptitudes de communication orale et écrite (français et anglais nécessaires) pour présenter aux congrès et rédiger des articles dans des revues scientifiques. Nous recherchons un jeune chercheur qui saura s’impliquer dans son projet, curieux, ayant une certaine autonomie et une forte motivation pour développer des compétences en synthèse et évaluation de comportements dans le domaine de l’interaction homme-robot. De plus, le candidat devra être apte à travailler en équipe sur des projets pluridisciplinaires.

Les candidatures devront inclure un CV détaillé ; au moins deux références (personnes susceptibles d’être contactées) ; une lettre de motivation d’une page ; un résumé d’une page du mémoire de master ; les notes de Master 1 ou 2 ou d’école d’ingénieur.

La date limite pour l’envoi des candidatures est le 15/7/2019

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Researcher position (W/M) at IRCAM

Fixed-term contract of 12 months

INTRODUCTION TO IRCAM:

IRCAM is a leading non-profit organization associated to Centre Pompidou, dedicated to music production,

scientific research and education in sound and music technologies. In the joint research unit, UMR 9912

STMS (Science and Technology for Music and Sound), supported by IRCAM, Sorbonne University, and the

CNRS, specialized research teams conduct research and development in the areas of acoustics, audio

signal processing, human-computer interaction, computer music and musicology. IRCAM is located in the

centre of Paris near the Centre Pompidou, at 1, Place Igor Stravinsky 75004 Paris.

POSITION DESCRIPTION:

IRCAM is looking for a researcher for the development of music content analysis technologies (such as

instrument recognition, audio quality, auto-tagging) in the framework of a technology transfer to the

Universal Music Group.

The hired researcher will also collaborate with the development team and participate in the project activities

(evaluation of technologies, meetings, specifications, reports).

REQUIRED EXPERIENCES ET SKILLS:

 Very high skills in audio signal processing (spectral analysis, audio-feature extraction, parameter

estimation) (the candidate should preferably hold a PhD in this field) ;

 Very high skills in machine learning (SVM, ConvNet) (the candidate should preferably hold a PhD in

this field) ;

 Good skills in distributed computing ;

 High-skills in Matlab and Python programming, skills in C/C++ programming ;

 Good knowledge of UNIX environments (GNU-Linux ou MacOSX) ;

 High productivity, methodical works, excellent programming style.

SALARY: According to background and experience.

Deadline for application: June, 17th, 2019

Please send an application letter with the reference 201906UMGRES together with your resume and any

suitable information addressing the above issues preferably by email to: mignot at ircam dot fr with cc to

vinet at ircam dot fr and roebel at ircam dot fr.

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PhD title: Decentralized collaborative learning for speech recognition in

a context of privacy protection

PhD project

The candidate will contribute to the DEEP-PRIVACY project funded by the French national

research agency (ANR). DEEP-PRIVACY aims at developing private-by-design acoustic

models for use in automatic speech recognition systems. Indeed, more and more devices

–mobile or not– invest our daily lives. These devices use voice technology to interact with

human users who can access IT services through natural interaction (Apple Siri, Amazon

Alexa, Google Home ...). With the stated goal of improving the quality of their services,

which includes automatic speech recognition, companies that offer such solutions usually

collect private audio data from these devices. This constitutes a real risk for privacy.

The PhD proposal is to study and propose approaches for distributed and decentralized

learning of neural acoustic models in which user data remain on the user's device.

A first objective consists in experimenting and comparing different approaches from the

literature to the particular case of acoustic models in speech processing tasks. We will

focus on machine learning algorithms where data are collected locally on every peer and

are not transmitted to a central server. Communications are restricted to models or

updates of weights computed locally on the device. The cases of centralized federated

learning approaches [Konečny et al. 2016, Leroy et al. 2018] and purely decentralized

approaches [Bellet et al. 2017] will be studied. Comparisons will include several trade-offs

involving model size and communication costs for instance.

Beyond the implementation and experimentation of federated learning and distributed

collaborative learning approaches for automatic speech recognition, a study on the nature

of the information conveyed (paralinguistics, phonetics, lexics) during these exchanges will

also be conducted to assess the level of privacy protection provided by the proposed

approaches. More precisely, it will be a question of studying the possibility of recognizing,

at a level that will have to be quantified if necessary, the speaker and the phonemes, even

the words uttered, by analyzing the values of the exchanged updates.

We will also investigate how to locally adapt the acoustic models to the voice and speech

characteristics of the user in order to obtain personalized models. The amount of data and

adaptation time will be studied.

In general, the thesis will keep a critical eye on the advantages and disadvantages offered

by collaborative automatic learning in a real application framework [Bhowmick et al. 2018;

Hitaj et al. 2018]. In view of the results of these experiments and analyses, the purpose of

the thesis will be to propose effective solutions to overcome the disadvantages and

propose an effective framework, linking privacy protection and improvement of acoustic

modeling for speech recognition in a distributed deployment context.

Technical skills required:

•Master’s degree in machine learning or in computer science

•Background in deep learning, and in statistics

•Experience with deep learning tools is a plus (preferably PyTorch)

•Good programing skills (preferably in Python)

•Experience in speech and/or speaker recognition is a plus

General information

This PhD thesis fits within the scope of a collaborative project (project DEEP-PRIVACY,

funded by the French National Research Agency) involving the LIA (Laboratoire

Informatique d’Avignon), the MAGNET team of Inria Lille - Nord Europe, the LIUM

(Laboratoire d'Informatique de l'Université du Mans) and the MULTISPEECH team of Inria

Nancy - Grand-Est.

This PhD position is in collaboration with Avignon University, and will be co-supervised by

Yannick Estève (https://cv.archives-ouvertes.fr/yannick-esteve) and Marc Tommasi (http://

researchers.lille.inria.fr/tommasi). The work location will mainly be in Avignon (LIA, Avignon

University), with some stays in Lille (CRIStAL / INRIA Lille Nord Europe).

Knowledge of the French language is not required.

Additional information:

Three-year work contract, with a monthly net salary of approximately 1685€/month, and a

financial support for international research training and conference participation plus a

contribution to the research costs.

Duration: 3 years

Starting date: September or October 2019

Contacts:

Yannick ESTÈVE yannick.esteve@univ-avignon.fr

Marc TOMMASI marc.tommasi@inria.fr

Doctoral School: Sciences et Agrosciences (Avignon University)

References

[Bellet et al. 2017] Bellet, A., Guerraoui, R., Taziki, M., & Tommasi, M. (2017). Personalized and private peerto-

peer machine learning. arXiv preprint arXiv:1705.08435

[Bhowmick et al. 2018] A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers. Protection Against

Reconstruction and Its Applications in Private Federated Learning. arXiv preprint https://arxiv.org/abs/

1812.00984

[Hitaj et al. 2018] B. Hitaj, G. Ateniese, F. Perez-Cruz. Deep Models Under the GAN: Information Leakage

from Collaborative Deep Learning. arXiv preprint https://arxiv.org/abs/1702.07464

[Konečný et al. 2016] J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, & D. Bacon. (2016).

Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv: https://arxiv.org/

abs/1610.05492

[Leroy et al. 2018] D. Leroy, A. Coucke, T. Lavril, T. Gisselbrecht, J. Dureau, Federated Learning for Keyword

Spotting. arXiv preprint https://arxiv.org/abs/1810.05512

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Modélisation multimodale de l’expressivité et de l’alignement pour l’interaction humain-machine

Directrice de thèse : Catherine Pelachaud (ISIR)

Co-encadrant : Nicolas Obin (STMS)

Contexte

Cette thèse s’inscrit dans un contexte particulièrement riche en développement d’interfaces de communication entre l’humain et la machine. Par exemple, l’émergence et la démocratisation des assistants personnels (smartphones, home assistants, chatbots) font de l’interaction avec la machine une réalité quotidienne pour de plus en plus d’individus. Cette pratique tend à s’amplifier et à se généraliser à un grand nombre d’usages et de pratique de l’être humain : depuis les agents d’accueil (aujourd’hui, quelques robots Pepper plus pour la démo que pour un usage réel), la consultation à distance, ou les agents embarqués dans les véhicules autonomes. L’expansion des usages appelle à une extension des modalités d’interaction et à l’amélioration de la qualité de l’interaction avec la machine : aujourd’hui, la voix constitue la modalité privilégiée de l’interaction, et les scénarios d’interaction demeurent très limités (demande d’information, question-réponse, pas de réelle interaction dans la durée). Les limitations principales sont d’une part une faible expressivité : le comportement de l’agent est encore souvent monomodal (voix, comme les assistants Alexa ou Google Home) et demeure très monotone, ce qui limite grandement l’acceptabilité, la durée et la qualité de l’interaction ; et d’autre part le comportement de l’agent est peu ou pas adapté à l’interlocuteur, ce qui diminue l’engagement de l’humain dans l’interaction. Lors d’une interaction humain-humain les phénomènes d’alignement (e.g., ton de voix, vitesse de mouvement corporel) sont des indices de compréhension commune et d’engagement dans l’interaction (Pickering et Garrod, 1999; Castellano et al, 2012). L’engagement est marqué par des comportements non-verbaux sociaux (nonverbal social behaviors) à des moments spécifiques de l’interaction : ce peut être des signaux de feedbacks (pour indiquer être en phase avec l’interactant), ou bien une forme d’imitation (par exemple : un sourire appelle un autre sourire, le ton de la voix reprend des éléments de celui de l’interactant), ou encore des signaux synchronisés avec ceux de l’interactant (la gestion des tours de parole). Cette thèse vise à modéliser le comportement de l’agent en fonction de celui de l’utilisateur pour qu’il puisse montrer son engagement attentionnel en vue de maintenir l’interaction et de rendre ses messages plus compréhensifs. L’adaptation du comportement de l’agent se produira à différents niveaux comportementaux (prosodique, lexicale, comportementale, imitation, tour de parole…). L’interaction humain-machine, avec un fort potentiel applicatif dans de nombreux domaines, est un exemple d'interdisciplinarité nécessaire entre humanités numériques, robotique, et intelligence artificielle.

Objectif

L’objectif de la thèse est de mieux comprendre et à modéliser les mécanismes qui régissent l’interaction multimodale (voix et geste) entre un humain et une machine, pour permettre de lever des verrous technologiques et permettre d’élaborer un agent conversationnel capable de s’adapter de manière naturelle et cohérente à un interactant humain.

1) Expressifs (Léon, 1993) : capable d’avoir une expression variée et cohérente pour maintenir l’attention de l’interlocuteur, souligner les points importants, améliorer la qualité de l’interaction et en allonger la durée (dépasser un ou deux tours de parole)

2) Alignés sur le comportement multimodal de l’interlocuteur (Pickering et Garrod, 1999; Castellano et al, 2012; Clavel et al, 2016) : c’est-à-dire capable d’adapter son comportement en fonction du comportement de l’interlocuteur, pour renforcer l’engagement de ce dernier dans l’interaction.

Dans un premier temps, la thèse proposera de réaliser une architecture neuronale unifiée pour la modélisation générative du comportement multimodale de l’agent. L’expressivité de l’agent, prosodique (Obin, 2011; Obin, 2015) et gestuelle (Pelachaud, 2009), sera modélisée par des architectures neuronales récurrentes aujourd'hui couramment utilisées pour la voix et le geste (Bahdanau et al, 2014, Wang, 2017, Robinson & Obin, 2019). La thèse se focalisera sur deux aspects essentiels de la modélisation du comportement de l’agent : le développement d’architectures structurées sur plusieurs échelles temporelles pour améliorer la modélisation de la variabilité prosodique et gestuelle à l’échelle de la phrase et à l’échelle du discours (Le Moine & Obin, 2019), et l’apprentissage d’un comportement multimodal cohérent par l’approfondissement de mécanismes d’attention multimodaux partagés appliqués à la synchronicité des profils prosodiques et gestuels générés (He, 2018).

Dans un deuxième temps, la thèse s’attaquera à l’alignement du comportement de l’agent avec celui de l’humain. La thèse approfondira particulièrement l’apprentissage interactif et par imitation pour adapter de manière cohérente le comportement multimodal de l’agent à l’humain (Weber, 2018; Mancini, 2019), à partir des bases de données de dialogues accessibles (telles que NoXi (récoltées à l’ISIR et annotées en terme d’engagement), IEMOCAP (USC, Carlos Busso), Gest-IS (Edinburgh University, Katya Saint-Amard)) pour apprendre la relation et aussi leur adaptation au cours de l'interaction entre les profils prosodiques et comportementaux des interlocuteurs.

La thèse sera co-encadrée par Catherine Pelachaud, de l’équipe PIRoS de l’ISIR, spécialisée en interaction humain-machine et agents conversationnels, et par Nicolas Obin, de l’équipe Analyse et Synthèse des Sons (AS) de STMS, spécialisée en modélisation générative de signaux de parole.. Le doctorant bénéficiera par ailleurs des connaissances, savoir-faire, et outils existants à STMS et à l’ISIR (par exemple : synthétiseur de parole ircamTTS développé à STMS, plateforme GRETA développée à l’ISIR) et de la logistique de calcul de STMS (serveur de calculs, GPU).

Bibliographie

(Bevacqua et al., 2012) Elisabetta Bevacqua, Etienne de Sevin, Sylwia Julia Hyniewska, Catherine Pelachaud, A listener model: Introducing personality traits, Journal on Multimodal User Interfaces, special issue Interacting ECAs, Elisabeth André, Marc Cavazza and Catherine Pelachaud (Guest Editors), July 2012, 6(1-2), pp 27-38.

(Castellano et al., 2012) G. Castellano, M. Mancini, C. Peters, P. W. McOwan. Expressive copying behavior for social agents: a perceptual analysis. IEEE Trans Syst, Man Cybern, Part A: Syst Hum 42(3), 2012.

(Clavel et al., 2016) Chloé Clavel, Angelo Cafaro, Sabrina Campano, and Catherine Pelachaud, Fostering user engagement in face-to-face human-agent interactions, in A. Esposito and L. Jain (Eds), Toward Robotic Socially Believable Behaving Systems - Volume I: Modeling Social Signals, Springer Series on Intelligent Systems Reference Library (ISRL), 2016

(Glas and Pelachaud, 2015) N. Glas, C. Pelachaud, Definitions of Engagement in Human-Agent Interaction, workshop ENHANCE, in International Conference on Affective Computing and Intelligent Interaction (ACII), 2015.

(Hall et al., 2005) L. Hall, S. Woods, R. Aylett, L. Newall, A. Paiva. Achieving empathic engagement through affective interaction with synthetic characters. Affective computing and intelligent interaction, 2005.

(He, 2018) Xiaodong He, Deep Attention Mechanism for Multimodal Intelligence: Perception, Reasoning, & Expression across Language & Vision, Microsoft Research, AI NEXTCon, 2018.

(Le Moine & Obin, 2019) Clément Lemoine, Modélisation neuronale de l’expressivité pour la transformation de la voix, stage de Master, 2019.

(Léon, 1993) P. Léon. Précis de phonostylistique : Parole et expressivité. Paris:Nathan, 1993.

(Obin, 2011) N. Obin. MeLos: Analysis and Modelling of Speech Prosody and Speaking Style, PhD. Thesis, Ircam-Upmc, 2011.

(Obin, 2015) N. Obin, C. Veaux, P. Lanchantin. Exploiting Alternatives for Text-To-Speech Synthesis: From Machine to Human, in Speech Prosody in Speech Synthesis: Modeling and generation of prosody for high quality and flexible speech synthesis. Chapter 3: Control of Prosody in Speech Synthesis, p.189-202, Springer Verlag, February, 2015.

(Ochs et al., 2008) M. Ochs, C. Pelachaud, D. Sadek, An Empathic Virtual Dialog Agent to Improve Human-Machine Interaction, Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Estoril Portugal, May 2008.

(Paiva et al., 2017) A. Paiva, I. Leite, H. Boukricha, Hana I. Wachsmuth 'Empathy in Virtual Agents and Robots: A Survey.', ACM Trans. Interact. Intell. Syst. (2017), 7 (3):11:1-11:40.

(Pelachaud, 2009) C. Pelachaud, Studies on Gesture Expressivity for a Virtual Agent, Speech Communication, special issue in honor of Björn Granstrom and Rolf Carlson, 51 (2009) 630-639.

(Poggi, 2007) I. Poggi. Mind, hands, face and body: a goal and belief view of multimodal communication. Weidler, Berlin, 2007.

(Robinson & Obin, 2019) C. Robinson, N. Obin, A. Roebel. Sequence-to-sequence modelling of F0 for speech emotion conversion, in IEEE International Conference on Audio, Signal, and Speech Processing (ICASSP), 2019.

(Sadoughi et al., 2017) Najmeh Sadoughi, Yang Liu, and Carlos Busso, 'Meaningful head movements driven by emotional synthetic speech,' Speech Communication, vol. 95, pp. 87-99, December 2017.

(Sidner and Dzikovska, 2002) C. L. Sidner, M. Dzikovska. Human-robot interaction: engagement between humans and robots for hosting activities. In: IEEE int conf on multimodal interfaces, 2002.

(Wang, 2017) Xin Wang, Shinji Takaki, Junichi Yamagishi. An RNN-Based Quantized F0 Model with Multi-Tier Feedback Links for Text-to-Speech Synthesis, Interspeech, 2017

(Wang, 2018) Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Fei Re, Ye Jia, Rif A. Saurous. « Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis », 2018.

(Weber, 2018) K. Weber, H. Ritschel, I. Aslan, F. Lingenfelser, E. André, How to Shape the Humor of a Robot - Social Behavior Adaptation Based on Reinforcement Learning, ACM International Conference on Multimodal Interaction, 2018.

(Mancini, 2019) M. Mancini, B. Biancardi, S. Dermouche, P. Lerner, C. Pelachaud, Managing Agent’s Impression Based on User’s Engagement Detection, Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, 2019.

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Thesis title

Privacy preserving and personalized transformations for speech recognition

This PhD thesis fits within the scope of a collaborative project (funded by the French National Research Agency) involving several French teams, among which, the MULTISPEECH team of Inria Nancy - Grand-Est and the LIUM (Laboratoire d'Informatique de l'Université du Mans).

This PhD position is in collaboration between the Multispeech team of the LORIA laboratory (Nancy) and Le Mans University. The thesis will be co-supervised by Denis Jouvet (https://members.loria.fr/DJouvet/) and Anthony Larcher (https://lium.univlemans.fr/team/anthony-larcher/). The selected candidate is expected to spend time in both teams over the course of the PhD.

Scientific Context

Over the last decade, great progress has been made in automatic speech recognition [Saon et al., 2017; Xiong et al., 2017]. This is due to the maturity of machine learning techniques (e.g., advanced forms of deep learning), to the availability of very large datasets, and to the increase in computational power. Consequently, the use of speech recognition is now spreading in many applications, such as virtual assistants (as for instance Apple’s Siri, Google Now, Microsoft’s Cortana, or Amazon’s Alexa) which collect, process and store personal speech data in centralized servers, raising serious concerns regarding the privacy of the data of their users. Embedded speech recognition frameworks have recently been introduced to address privacy issues during the recognition phase: in this case, a (pretrained) speech recognition model is shipped to the user's device so that the processing can be done locally without the user sharing its data. However, speech recognition technology still has limited performance in adverse conditions (e.g., noisy environments, reverberated speech, strong accents, etc.) and thus, there is a need for performance improvement. This can only be achieved by using large speech corpora that are representative of the actual users and of the various usage conditions. There is therefore a strong need to share speech data for improved training that is beneficial to all users, while preserving the privacy of the users, which means at least keeping the speaker identity and voice characteristics private1.

1 Note that when sharing data, users may want not to share data conveying private information at the linguistic level (e.g., phone number, person name, …). Such privacy aspects also need to be taken into account, but they are out-of-the scope of this thesis.

Missions: (objectives, approach, etc.)

Within this context, the objective of the proposed thesis is twofold. First, it aims at finding a privacy preserving transform of the speech data, and, second, it will investigate the use of additional personalized transforms, that can be applied on the user’s terminal, to increase speech recognition performance.

In the proposed approach, the device of each user will not share its raw speech data, but a privacy preserving transformation of the user speech data. In such approach, some private computations will be handled locally, while some cross-user computations may be carried out on a server using the transformed speech data, which protect the speaker identity and some of his/her features (gender, sentiment, emotions...). More specifically, this will rely on a representation learning to separate the features of the user data that can expose private information from generic ones useful for the task of interest, i.e., here, the recognition of the linguistic content. We will build upon ideas of Generative Adversarial Networks (GANs) for proposing such a privacy preserving transform. Since a few years, GANs are getting more and more used in deep learning. They

typically rely on both a generative network and a discriminative network, where the generator aims to output samples that the discriminator cannot distinguish from the true samples [Goodfellow et al., 2014; Creswell et al., 2018]. They have also been used as autoencoders [Makhzani et al., 2015] which are made of three mains blocks: encoder, generator and discriminator. In our case, the discriminators shall focus on discriminating between speakers and/or between voice-related classes (defined according to gender, emotions, etc). The training objective will be to maximize the speech recognition performance (using the privacy preserving transformed signal) while minimizing the available speaker or voice-related information measured by the discriminator.

As devices are getting more and more personal, it creates opportunities to make speech recognition more personalized. This includes two aspects: adapting the model parameters to the speaker (and to the device) and introducing personalized transforms to help hiding the speaker voice identity. Both aspects will be investigated. Voice conversion approaches provide example of transforms aiming at modifying the voice of a speaker so that it sounds like the voice of another target speaker [e.g., Chen et al., 2014; Mohammadi & Kain, 2014]. Similar approaches can thus be applied to map speaker specific features to those of a standard (or average) speaker, which thus would help concealing its identity. To take benefit of the increased personal usage of terminals, speaker and environment specific adaptation will be investigated to improve speech recognition performance. Collaborative learning mixing speech and speaker recognition has been shown to benefit both tasks [Liu et al. 2018; Garimella et al. 2015] and provide a way to combine both information in a single framework. This approach will be compared to adaptation of deep neural networks-based models [e.g., Abdel-Hamid & Jiang, 2013] to handle best different amounts of adaptation data.

Skills and profile:

Master in machine learning or in computer science

Background in statistics, and in deep learning

Experience with deep learning tools is a plus

Good computer skills (preferably in Python)

Experience in speech and/or speaker recognition is a plus

Bibliography:

[Abdel-Hamid & Jiang, 2013] Abdel-Hamid, O., & Jiang, H. Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code. In ICASSP-2013, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7942-7946, 2013.

[Chen et al., 2014] Chen, L. H., Ling, Z. H., Liu, L. J., & Dai, L. R. Voice conversion using deep neural networks with layer-wise generative training. TASLP-2014, IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(12), pp. 1859-1872, 2014.

[Creswell et al., 2018] Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., and Bharath, A. A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine 35, 1, 53-65, 2018.

[Garimella et al. 2015] Garimella, S., Mandal, A., Strom, N., Hoffmeister, B., Matsoukas, S., & Parthasarathi, S. H. K., Robust i-vector based adaptation of DNN acoustic model for speech recognition. In INTERSPEECH, 2015.

[Goodfellow et al., 2014] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In Advances in neural information processing systems, pp. 2672-2680, 2014.

[Liu et al. 2018] Y. Liu, L. He, J. Liu, and M. Johnson, Speaker Embedding Extraction with Phonetic Information,' in INTERSPEECH , pp. 2247-2251, 2018

[Makhzani, 2015] Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.

[Mohammadi & Kain, 2014] Mohammadi, S. H., & Kain, A. Voice conversion using deep neural networks with speaker-independent pre-training. In SLT-2014, Spoken Language Technology Workshop , pp. 19-23, 2014.

[Saon et al., 2017] G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim, B. Roomi, and P. Hall. English conversational telephone speech recognition by humans and machines. Technical report, arXiv:1703.02136, 2017.

[Xiong et al., 2017] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig. Achieving human parity in conversational speech recognition. Technical report, arXiv:1610.05256, 2017.

Additional information:

Supervision and contact:

Denis Jouvet (denis.jouvet@loria.fr; https://members.loria.fr/DJouvet/)

Anthony Larcher (anthony.larcher@univ-lemans.fr; https://lium.univlemans.fr/team/anthony-larcher/)

Additional link:

Ecole Doctorale IAEM Lorraine (http://iaem.univ-lorraine.fr/)

Duration: 3 years

Starting date: autumn 2019

The candidates are required to provide the following documents in a single pdf or ZIP file:

 CV

 A cover/motivation letter describing their interest in the topic

 Degree certificates and transcripts for Bachelor and Master (or the last 5 years)

 Master thesis (or equivalent) if it is already completed, or a description of the work in progress, otherwise

 The publications (or web links) of the candidate, if any (it is not expected that they have any)

 In addition, one recommendation letter from the person who supervises(d) the Master thesis (or research project or internship) should be sent directly by his/her author to the prospective PhD advisor.

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Open fully-funded PhD position: “Hybrid Bayesian and deep neural modeling for weakly supervised

learning of sensory-motor speech representations”

The Deep-COSMO project, part of the new AI institute in Grenoble, is welcoming applications for a 3-year, fully funded

PhD scholarship starting October, 1st, 2019 at GIPSA-lab (Grenoble, France)

TOPIC: Representation learning, speech production and perception, Bayesian cognitive models, generative neural

networks

RESEARCH FIELD: Computer Science, Cognitive Science, Machine Learning, Artificial Intelligence, Speech Processing

SUPERVISION: J. Diard (LPNC); T. Hueber, L. Girin, J.-L. Schwartz (GIPSA-Lab)

IDEX PROJECT TITLE: Multidisciplinary Institute for Artificial Intelligence – Speech chair (P. Perrier)

SCIENTIFIC DEPARTMENT (LABORATORY’S NAME): GIPSA-lab

DOCTORAL SCHOOL: MSTII (maths and computer science) or EEATS (signal processing) or EDISCE (cognitive science),

depending on the candidate’s profile and career plan

TYPE of CONTRACT: 3-year doctoral contract

JOB STATUS: Full time

HOURS PER WEEK: 35

SALARY: between 1770 € and 2100 € gross per month (depending on complementary activity or not)

OFFER STARTING DATE: October, 1st, 2019

SUBJECT DESCRIPTION:

General objective

How can a child learn to speak from hearing sounds, without any motor instruction provided by his/her environment?

The general objective of this PhD project is to develop a computational agent, able to learn speech representations

from raw speech data in a weakly supervised configuration. This agent will involve an articulatory model of the human

vocal tract, an articulatory-to-acoustic synthesis system, and a learning architecture combining deep learning

algorithms and developmental principles inspired from cognitive sciences. This PhD will be part of the “Speech

communication” chair in the Multidisciplinary Institute for Artificial Intelligence in Grenoble (MIAI).

Method

This work will capitalize on two bricks of research recently developed in Grenoble. First, a Bayesian computational

model of speech communication called COSMO (Communicating about Objects using SensoriMotor Operations)

(Moulin-Frier et al., 2012, 2015; Laurent et al., 2017; Barnaud et al., 2019) was jointly developed by GIPSA and LPNC.

This model associates speech production and speech perception models in a single architecture. The random variables

in COSMO represent the signals and the sensori-motor processes involved in the speech production/perception loop.

COSMO learns their probability distributions from speech examples provided by the environment, and is then able to

perceive and produce speech sounds associated to speech categories. So far, COSMO was mostly tested on synthetic

data. One of the main challenges is now to confront COSMO to real-world data.

Second, we will also capitalize on a set of computational models for automatic processing and learning of sensorymotor

distributions in speech developed at GIPSA. This comprises a set of transfer-learning algorithms (Hueber et

al., 2015, Girin et al. 2017) aiming at adapting acoustic-articulatory knowledge on one speaker, towards another

speaker, using a limited amount of data, possibly incomplete and noisy; together with a set of deep neural networks

able to process raw articulatory data (Hueber et al., 2016; Tatulli & Hueber, 2017).

The first step will consist in designing, implementing and testing a “deep” version of COSMO, in which some of the

probability distributions are implemented by generative neural models (e.g. VAE, GAN). This choice is motivated by

the ability of such techniques to deal with raw, noisy and complex data, as well as their flexibility in terms of transfer

learning. The second stage will consist in reformulating entirely the speech communication agent in an end-to-end

neural architecture.

Outputs

The system will be tested in terms of both efficiency of the learning process – hence ability to generate realistic speech

sequences after convergence – and coherence of the motor strategies discovered by the computational agent, in spite

of the fact that no motor data will be provided for learning. The outputs are both (1) theoretical – for better

understanding the cognitive processes at hand in speech development and speech communication; (2) technical – for

integrating knowledge about speech production and cognitive processes in a machine learning architecture; and (3)

technological – for proposing a new generation of autonomous speech technologies.

ELIGIBILITY CRITERIA

Applicants must have:

- A Master's degree (or be about to earn one) or have a university degree equivalent to a European Master's (5-year

duration), in Computer Science, Cognitive Science, Signal Processing or Applied Mathematics.

- Solid skills in Machine Learning or probabilistic modeling + General knowledge in natural language processing

and/or speech processing (an affinity for cognitive sciences and speech sciences is welcome).

- Very good programming skills (mostly in Python).

- Good oral and written communication in English.

- Ability to work autonomously and in collaboration with supervisors and other team members.

SELECTION PROCESS

Applicants will have to send their CV + an application letter in English + copy of their last diploma to:

Jean-Luc.Schwartzr@gipsa-lab.fr, Thomas.Hueber@gipsa-lab.fr;

Letters of recommendation are welcome. Contact before preparing a complete application are welcome too.

Applications will be evaluated as they are received: the position is open until it is filled, with deadline on July 10th, 2019

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PhD thesis proposal

Incremental sequence-to-sequence mapping for

speech generation using deep neural networks

June 17, 2019

1 Context and objectives

In recent years, deep neural networks have been widely used to address sequence-

to-sequence (S2S) learning. S2S models can solve many tasks where source

and target sequences have di
erent lengths such as: automatic speech recog-

nition, machine translation, speech translation, text-to-speech synthesis, etc.

Recurrent, convolutional and transformer architectures, coupled with attention

models, have shown their ability to capture and model complex temporal de-

pendencies between a source and a target sequence of multidimensional discrete

and/or continuous data. Importantly, end-to-end training alleviates the need

to previously extract handcrafted features from the data by learning hierarchi-

cal representations directly from raw data (e.g. character string, video, speech

waveform, etc.).

The most common models are composed of an encoder that reads the full in-

put sequence (i.e. from its beginning to its end) before the decoder produces the

corresponding output sequence. This implies a latency equals to the length of

the input sequence. In particular, for a text-to-speech (TTS) system, the speech

waveform is usually synthesized from a complete text utterance (e.g. a sequence

of words with explicit begin/end-of-utterance markers). Such approach cannot

be used in a truly interactive scenario, in particular by a speech-handicapped

person to communicate orally'. Indeed, the interlocutor has to wait for the

complete utterance to be typed before being able to listen to the synthetic voice,

hence limiting the dynamics and naturalness of the interaction.

The goal of this project is to develop a general methodology for incremental

sequence-to-sequence mapping, with application to interactive speech technolo-

gies. It will require the development of end-to-end classication and regression

neural models able to deliver chunks of output data on-the-y, from only a par-

tial observation of input data. The goal is to learn an ecient policy that leads

to an optimal trade-o
between (variable) latency and accuracy of the decoding

process. Possible strategies to decode the output data as soon as possible in-

clude: (i) Predicting online he future' of the output sequence from he past

1

and present' of the input sequence, with an acceptable tolerance to possible er-

rors, or (2) learn automatically from the data an optimal waiting policy' that

prevents the model to output data when the uncertainty is too high. The devel-

oped methodology will be applied to address two speech processing problems:

(i) Incremental Text-to-Speech synthesis in which speech is synthesized while

the user is typing the text (possibly with a variable latency), and (ii) Incremen-

tal speech enhancement/inpainting in which portions of the speech signal are

unintelligible because of sudden noise or speech production disorders, and must

be replaced on-the-y with reconstructed portions.

2 Work plan

The proposed working plan is the following :

 Bibliographic work on S2S neural models, in the context of speech recogni-

tion, speech synthesis, and machine translation as well as their incremental

(low-latency) variations

 Investigating new architectures, losses, and training strategies toward in-

cremental S2S models.

 Implementing and evaluating the proposed techniques in the context of

end-to-end neural TTS systems (the baseline system may be a neural

TTS trained with past information/left-context only).

 Implementing and evaluating the proposed techniques in the context of

speech enhancement/inpainting, rst on simulated noisy speech and then

on pathological speech.

3 Requirements

We are looking for an outstanding and highly motivated PhD candidate to work

on this subject. Following requirements are mandatory:

 Engineering degree and/or a Master's degree in Computer Science, Signal

Processing or Applied Mathematics.

 Solid skills in Machine Learning. General knowledge in natural language

processing and/or speech processing.

 Excellent programming skills (mostly in Python and deep learning frame-

works).

 Good oral and written communication in English.

 Ability to work autonomously and in collaboration with supervisors and

other team members.

2

4 Work context

Grenoble Alpes Univ. o
ers an excellent research environment with ample com-

puting facilities, as well as remarkable surroundings to explore over the week-

ends. The PhD project will be funded by the Grenoble Articial Intelligence

Institute (MIAI). The PhD candidate will work both at GIPSA-lab (CRISSP

team) and LIG-lab (GETALP team). The duration of the PhD is 3 years. The

salary is between 1770 and 2100 euros gross per month (depending on comple-

mentary activity or not).

5 How to apply?

Applications should include a detailed CV; a copy of their last diploma; at least

two references (people likely to be contacted); a cover letter of one page; a one-

page summary of the Master thesis; the two last transcripts of notes (Master or

engineering school). Applications should be sent to thomas.hueber@gipsa-lab.fr,

laurent.girin@gipsa-lab.fr and laurent.besacier@imag.fr. Applications will be

evaluated as they are received: the position is open until it is lled, with deadline

on July 10th, 2019.

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 POST-DOC POSITION (18 months) - Forensic Voice Comparison (VoxCrim project): ability, limitations and specificities of listeners in speaker identification tasks
Laboratoire Parole et Langage (CNRS and Aix-Marseille Université) ? Aix-en-Provence, France

CONTEXT
The post-doc will be carried out within the framework of the VoxCrim project, funded by an ANR (Agence nationale de la recherche) grant (2017-2021, https://anr.fr/Project-ANR-17-CE39-0016). VoxCrim focuses on national security and legal/justice applications and aims to provide a validated scientific objective framework for all types of forensic voice comparison methods (automatic and phonetic). The goal is to develop certified standards to determine the specific areas in which voice comparison methods are applicable. The project includes two complementary subject areas: 1. the proposal of methodological standards to homogenize the expertise of voice comparison in a judicial environment, 2. the development of basic research in the fields of automatic speech processing and phonetics (speaker characteristics in the production and perception of speech).
The post-doc will participate in the second subject area (speaker characteristics in the production and perception of speech). Two questions need to be answered: What are the abilities and limits of listeners in speaker identification tasks? Which cues do listeners use to identify speakers?

TASKS
The main objective will be to conduct perception experiments aimed at assessing the ability of listeners in several speaker identification tasks.
The post-doctoral fellow will:
-    design experimental protocols
-    create and manipulate acoustic stimuli
-    run experiments and collect data
-    process data and perform statistical analysis
Finally, results will be presented at conferences and published in international journals.

WORK ENVIRONNEMENT
The postdoctoral fellow will work at the Laboratoire Parole et Langage (http://www.lpl-aix.fr/), a laboratory whose research interests are extremely varied (including linguistics, phonetics, neuroscience, psycholinguistics, sociolinguistics, and computer science). He or she will benefit from this stimulating environment and interact with all the members of the laboratory (faculty members, other post-docs, engineers, doctoral students, etc.). He or she will have the opportunity to discover all the projects of the laboratory.

EXPECTED PROFILE
The postdoctoral fellow will have a PhD in the speech sciences and/or in psychoacoustics (auditory measurements, audio signal processing). A strong background in data processing and statistics is also be required. A good command of French and English will also be appreciated.

18 months. Beginning in autumn 2019
Monthly salary: ~?1900  net (depending on experience)
Location : Laboratoire Parole et Langage (http://www.lpl-aix.fr/), Aix Marseille Université, CNRS UMR 7309, Aix-en-Provence, France
For additional information: christine.meunier@univ-amu.fr

-Application: CV and cover letter
-Send application as soon as possible to christine.meunier@univ-amu.fr

Supervisors: Dr. Christine Meunier, CNRS Researcher and Dr. Alain Ghio, Research Engineer - Laboratoire Parole et Langage, Aix-en-Provence, France.

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RECRUTEMENT D?UN INGÉNIEUR D?ÉTUDES EN TRAITEMENT AUTOMATIQUE DES LANGUES NATURELLES ET EN DÉVELOPPEMENT D?UNE INTERFACE IHM-WEB

Début de contrat: Octobre 2019
Durée: 7 mois

Salaire: 2000? brut/mois


Profil :
- Titulaire d?un Master ou d?un doctorat en TAL
- Une formation en sciences du langage sera appréciée.
- Compétences opérationnelles en génie logiciel (gestion de version, tests, qualité de code) et Python

- Des compétences en C/C++ seraient un plus

- Une expérience en traitement automatique de la parole est requise ainsi qu?un bon niveau de français.

- Une experience en METEOR, firepad, Node.JS, mongoDB, firebase serait un plus.

Ce poste nécessite des capacités de travail en équipe et en autonomie.

Une connaissance du contexte linguistique de la surdité serait un atout supplémentaire.

*Description du projet et des missions*
Dans le cadre du projet MANES (Médiation et Accessibilité Numérique pour les Étudiants Sourds) dirigé par François Portet (LIG), Isabelle Estève (LIDILEM) et Marion Fabre (ECP) dont une partie du financement est assurée par PULSALYS, IDEX Lyon-Saint Etienne, nous recrutons un ingénieur d?études pour un CDD de 7 mois.

L?objectif général du projet est de développer un dispositif de sous-titrage en temps-réel pour rendre accessible le discours oral de l?enseignant aux étudiants sourds, de façon à favoriser l?appropriation individuelle des savoirs, par le biais de la prise de notes. La réalisation technologique et les capacités de traitement de l?écrit par les publics sourds seront les deux axes de ce projet.

La mission de l?ingénieur d?études consiste d?une part à développer, évaluer et améliorer des prototypes basés sur les dernières avancées scientifiques et à les fusionner pour réaliser un prototype de sous-titrage automatique en temps réel, à partir de la plateforme Kaldi.

D?autre part, à concevoir une interface IHM pour la retranscription automatique en temps réel du discours de l?enseignant et la projection du sous-titrage en cours, en y intégrant le prototype mentionné ci-dessus.

Le candidat aura en charge l?élaboration du prototype pour des expérimentations en salle de cours et les réajustements de l?interface liés à ces expérimentations.

Missions en Traitement automatique des Langues :
- Prise de connaissance de l?état de l'art des systèmes de sous-titrage automatique
- Test de transcription semi-automatique et vérification des extraits oraux de cours magistraux
- Adaptation du système temps-réel de transcription automatique à réseaux de neurones (Kaldi)
- Traitement en temps-réel des transcriptions pour le sous-titrage adapté aux publics sourds

Les exigences fonctionnelles envisagées pour l?implémentation de la plateforme Kaldi sont : le repérage des mots-clés et des synonymes en temps réel (déjà existant dans KALDI) et le développement de nouvelles fonctionnalités : la segmentation et la simplification. Des perspectives d?adaptations pour le Off-Line seront aussi à envisager.


Missions en développement :
- Réalisation d?une application web permettant la retranscription en temps réel du discours de l?enseignant et la projection de la retranscription obtenue (vidéo projecteur et possibilité d?extension pour une interface mobile)
- Elaboration de l?interface étudiant : stockage, récupération de la trace écrite pour retravail et modification.
- Elaboration de l?interface enseignant : paramétrisation des éléments clés du cours
- Documentation : description et mode d?emploi de l?interface d?IHM.


*Environnement de travail*

Le projet est porté par le laboratoire Education, Cultures et Politiques (ECP, EA 4571), Université Lumière Lyon 2 encollaboration avec le Laboratoire de Linguistique et Didactique des Langues Étrangères et Maternelles (LIDILEM), Université Grenoble-Alpes et le Laboratoire d'Informatique de Grenoble (LIG).

Le poste sera accueilli physiquement au Laboratoire d'Informatique de Grenoble, UMR CNRS, au sein de l'équipe GETALP. L'équipe GETALP (https://lig-getalp.imag.fr) regroupe plus de 40 chercheurs, ingénieurs et étudiants dans le domaine du traitement automatisé des langues et de la parole multilingue.

*Candidature*
Envoyer un CV, une lettre de motivation et 1 à 3 lettres de recommandation à Marion.Fabre@univ-lyon2.fr, François.portet@imag.fr, Benjamin.Lecouteux@imag.fr et isabelle.esteve@univ-grenoble-alpes.fr.
Les candidatures seront examinées dès réception, la convocation éventuelle à un entretien d?embauche est prévue début juillet. Merci de candidater dès que possible et avant le 1^er Juillet 2019 minuit.

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ZAION est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l’Intelligence Artificielle.

ZAION a développé une solution qui s’appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 18 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP …).

Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c’est prendre part à une aventure passionnante au sein d’une équipe ambitieuse afin de devenir la référence sur le marché des robots conversationnels.

Dans le cadre de son développement ZAION recherche son Responsable de IA H/F. Manager de l’équipe R&D, votre rôle est stratégique dans le développement et l’expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu’ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse…) et donc adapter leurs réponses en conséquence.

Vos missions principales :

- Vous participez à la création du pôle R&D de ZAION et piloterez à votre arrivée votre premier projet de reconnaissance d’émotion dans la voix.

- Construisez, adaptez et faites évoluer nos services de détection d’émotion dans la voix

- Analysez de bases de données conséquentes de conversations pour en extraire les conversations émotionnellement pertinentes

- Construisez une base de données de conversations labelisées avec des étiquettes émotionnelles

- Formez et évaluez des modèles d'apprentissage automatique pour la classification d’émotion

- Déployez vos modèles en production

- Améliorez en continue le système de détection des émotions dans la voix

Qualifications requises et expérience antérieure :

-Vous avez une expérience de 5 ans minimum comme Data Scientist/Machine Learning appliqué à l’Audio et une appétence à l’encadrement 

- Diplômé d’une école d’Ingénieur ou Master en informatique ou un doctorat en informatique mathématiques avec des compétences solides en traitements de signal (audio de préférence)

- Solide formation théorique en apprentissage machine et dans les domaines mathématiques pertinents (clustering, classification, factorisation matricielle, inférence bayésienne, deep learning...)

- La mise à disposition de modèles d'apprentissage machine dans un environnement de production serait un plus

- Vous maîtrisez un ou plusieurs des langages suivants : Python, Frameworks de machine Learning/Deep Learning (Pytorch, TensorFlow,Sci-kit learn, Keras) et Javascript

- Vous maîtrisez les techniques du traitement du signal audio

- Une expérience confirmée dans la labélisation de grande BDD (audio de préférence) est indispensable ;

- Votre personnalité : Leader, autonome, passionné par votre métier, vous savez animer une équipe en mode projet

- Vous parlez anglais couramment

Merci d’envoyer votre candidature à : alegentil@zaion.ai

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Post-doc position open
------------------------------------
The Speech and Language Technology Group in Le Mans University is looking for
a post-doc scientist to develop autonomous systems

Keywords: Deep Learning, lifelong, autonomous systems, unsupervised learning,
active-learning, interactive-learning

Context
------------------------------------
The LST team from LIUM (Le Mans University) is focusing on autonomous system?s  behavior
for the task of speaker diarization and machine translation. The ALLIES project
(European Chist-ERA collaborative project) aims at developing evaluation protocols,
metrics
and scenarios for lifelong learning autonomous systems. The goal is to enable
auto-adaptable
systems that can also auto-evaluate in order to sustain their performance across time.
Autonomous systems can rely on human domain experts via active and interactive learning
processes to be define within the ALLIES project.

Missions
------------------------------------
Develop an autonomous system for speaker diarization by integrating lifelong learning,
active and interactive learning components. The research work will be related to some of
the following topics:
    - unsupervised adaptation
    - unsupervised evaluation
    - active learning (based on the unsupervised evaluation process, the autonomous system
        is free to require additional knowledge from the human domain expert)
    - Interactive learning (a human domain expert provides specific knowledge to the
autonomous
        system.  This  information must be taken into account by the system)
    - Performance will be analyzed using protocols, metrics and scenarios developed for
the ALLIES project.

Participation to the ALLIES benchmarking evaluation for speaker diarization.
During the ALLIES project, LIUM is organizing two international evaluation
campaigns (one for Speaker Diarization jointly organized with Albayzin and the
second one for Machine Translation jointly with WMT)
The benchmarking evaluation will serve to validate approaches developed during the
post-doc

Dissemination
The research will be published in the major conferences and journals

------------------------------------
Duration: 12 months
Salary: 2 365,14? (after taxes)

Start: as soon as possible, latest January 2020

Location: LIUM, Le Mans University

Supervisers: Anthony Larcher (anthony.larcher@univ-lemans.fr) and Loïc Barrault
(loic.barrault@univ-lemans.fr)

Expected competences:
    - Phd in Machine Learning and Deep Learning
    - Experience in speech processing is positive
    - Python fluent
    - familiar with a deep learning toolkit (Pytorch, TensorFlow)

ALLIES website: https://projets-lium.univ-lemans.fr/allies/

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1.SPEECH RECOGNITION INTERN

Location: Cambridge, UK

Contact: careers@speechmatics.com

“As an intern at Speechmatics I have worked on projects that use real machine learning to deliver real value to people across the world. There are few places where the machine learning being used is at the bleeding edge of the field, but Speechmatics is one of them. The company has an amazing culture that allows you to grow as a programmer and as a person. If you want to be a part of a fast-growing machine learning company where you, personally, will make a difference then Speechmatics could well be the place for you!” 

• Sam Ringer, Machine Learning Engineer (previously R&D Intern), Speechmatics 

Background

Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.

At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.

The Opportunity

The Speechmatics Engineering team develops and maintains speech-oriented products and services that will be used by businesses worldwide and is responsible for the complete product development cycle for these products. In this internship, you’ll help to support fundamental speech and language processing research to improve our performance and language coverage as well as helping to build products and features to delight our users.

Because you will be joining a rapidly expanding team, you will need to be a team player who thrives in a fast-paced environment, with a focus on investigating ideas and rapidly moving research developments into products. We strongly encourage versatility and knowledge transfer within and across teams. You will be expected to learn fast and feel emboldened to ask for support as you need it.

Prior experience of speech recognition is desirable, although Speechmatics has a team of speech recognition engineers who will collaborate and share any specialised knowledge required. If you are enthusiastic about speech recognition and machine learning in general, with the drive to deliver the best possible technology solutions, then we want to hear from you!

Our internships are not time constrained to specific dates – we can work out mutually agreeable start and end dates as part of the application process.

Key Responsibilities

• Exploring and evaluating research ideas

• Increasing and improving our language coverage

• Prototyping new and improved features

• Helping the company to take your R&D through to production

• Communicating your work internally

Requirements

Essential

• Team player

• Enthusiasm for speech recognition and machine learning

• Technical understanding of speech recognition or related discipline

• Ability to rapidly deliver on ideas

• Competent in Python and/or C/C++

• Have or be studying towards a degree involving speech recognition, machine learning / computer science or related field

Desirable

• Practical experience of ASR and ML packages such as Kaldi, HTK or TensorFlow

• Commercial experience of speech recognition

• Software development experience

Salary

Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.

Interested?

Get in touch! Send your CV and covering letter to careers@speechmatics.com.

2.SPEECH RECOGNITION ENGINEER

Location: Cambridge, UK

Contact: careers@speechmatics.com

'As a Speech Recognition Engineer at Speechmatics, I work on solving a multitude of problems related to improving the accuracy and delivering new features for a global automatic speech recognition engine. As a member of the speech team, I work across every aspect of speech and implement the latest research in acoustic and language modelling. The team is supportive and also rich in terms of skills and backgrounds. Speechmatics offer progressive and rewarding opportunities in one of the best speech technology companies in the world.' 

• André Mansikkaniemi, Speech Recognition Engineer at Speechmatics 

Background

Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.

At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.

The Opportunity 

We are looking for a talented speech engineer to help us build the best speech technology for anybody, anywhere, in any language. You will be part of a team that is working on our core ASR capabilities to improve our speed and accuracy and develop novel features that we can support in all languages. Your work will feed into our ground-breaking framework to support the building of ASR models in every language pack published by the company. You will be responsible for keeping our system the most accurate and useful commercial speech recognition system available. 

As you will be joining a small team, you will need to be a team player who thrives in a fast-paced environment, with a focus on rapidly moving research developments into products. Bringing skills into the team is as important as a can-do attitude. We strongly encourage versatility and knowledge transfer within the team, so that we can share efficiently what needs to be done to meet our commitments to the rest of the company. 

Key Responsibilities 

• Research and development of improved speed and accuracy across our range of world leading ASR products and related features

• Delivering the software that provides an easy-to-use feature rich ASR product for our customers

• Enhancing our machine learning framework that robustly builds any language with the best possible performance

• Taking data all the way from its raw form through to a finished model

• Working within a team in an agile environment

• Working closely with other technical teams and product team to deliver on the company’s technical vision

Requirements 

Essential 

• Graduate degree in Statistics, Engineering, Mathematics, Computer Science 

• Knowledge of key natural language processing or related technologies, such as speech recognition, text-to-speech or natural language understanding 

• Experience working with standard speech and/or ML toolkits, e.g. Kaldi, KenLM, TensorFlow, etc. 

• Solid Python programming skills

• Experience using Unix/Linux 

• Quick and enthusiastic learner

• Excellent teamwork and communications skills

• Analytical mind-set with a data-driven approach to making decisions and attention to detail 

Desirable 

• Postgraduate degree in related discipline 

• Commercial work experience in ASR or a related field 

• Experience of working in an Agile framework 

• Expertise in modern speech recognition, including WFSTs, lattice processing, neural net (RNN / DNN / LSTM), acoustic and language models, decoding 

• Comprehensive knowledge of machine learning and statistical modelling 

• Experience in deep machine learning and related toolkits, e.g. Theano, Torch, etc. 

• Deep expertise in Python and/or C++ software development 

• Experience working effectively with software engineering teams or as a Software Engineer 

Salary

Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.

Interested?

Get in touch! Send your CV and covering letter to careers@speechmatics.com.

3.SENIOR SPEECH RECOGNITION ENGINEER

Location: Cambridge, UK

Contact: careers@speechmatics.com

'As a Speech Recognition Engineer at Speechmatics, I work on solving a multitude of problems related to improving the accuracy and delivering new features for a global Automatic Speech Recognition engine. As a member of the speech team, I work across every aspect of speech and implement the latest research in acoustic and language modelling. The team is supportive and also rich in terms of skills and backgrounds. Speechmatics offer progressive and rewarding opportunities in one of the best speech technology companies in the world.'

• André Mansikkaniemi, Speech Recognition Engineer, Speechmatics

Background

Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.

At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.

The Opportunity

We are looking for a talented speech engineer to help us build the best speech technology for anybody, anywhere, in any language. You will be part of a team that is working on our core ASR capabilities to improve our speed and accuracy and develop novel features that we can support in all languages. Your work will feed into our ground-breaking framework to support the building of ASR models in every language pack published by the company. You will be responsible for keeping our system the most accurate and useful commercial speech recognition system available.

As you will be joining a small team, you will need to be a team player who thrives in a fast-paced environment, with a focus on rapidly moving research developments into products. Bringing skills into the team is as important as a can-do attitude. We strongly encourage versatility and knowledge transfer within the team, so that we can share efficiently what needs to be done to meet our commitments to the rest of the company.

Key Responsibilities

• Research and development of improved speed and accuracy across our range of world leading ASR products and related features

• Delivering the software that provides an easy-to-use feature rich ASR product for our customers

• Enhancing our machine learning framework that robustly builds any language with the best possible performance

• Taking data all the way from its raw form through to a finished model

• Working within a team in an agile environment

• Working closely with other technical teams and product team to deliver on the company’s technical vision

Requirements

Essential

• Commercial experience in ASR or a related field

• Graduate degree in Statistics, Engineering, Mathematics, or Computer Science

• Expertise in modern speech recognition, including WFSTs, lattice processing, neural net (RNN / DNN / LSTM), acoustic and language models, decoding

• Experience working with standard speech and/or ML toolkits, e.g. Kaldi, KenLM, TensorFlow, etc.

• Solid Python programming skills

• Experience using Unix/Linux

• Drive to help those around you learn and improve every day

• Excellent teamwork and communications skills

• Analytical mind-set with a data-driven approach to making decisions and attention to detail

Desirable

• Postgraduate degree in related discipline

• Experience of working in an Agile framework

• Comprehensive knowledge of machine learning and statistical modelling

• Experience in deep machine learning and related toolkits, e.g. Theano, Torch, etc.

• Deep expertise in Python and/or C++ software development

• Experience working effectively with software engineering teams or as a Software Engineer

Salary

Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.

Interested?

Get in touch! Send your CV and covering letter to careers@speechmatics.com.

More about Speechmatics’ culture

Live for the wow | Build authentic relationships | Be the adventure

Innovation is what we do. We build, we iterate, we develop the next thing that delivers that wow moment. We see value in building long-term, authentic relationships that last and are based on trust and honesty. With our customers, our colleagues, our leaders, our suppliers or within our local community. Our journey should be fun and exciting. We will celebrate our successes and learn from our mistakes together along the way. We embrace learning and change to grow naturally and organically as a company and individuals. We trust, we’re honest, kind and respectful.

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