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References
Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc
Delgado R., Stefancic J. (2014), ?Hate speech in cyberspace?, Wake Forest Law Review, 49.
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54.
Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112.
Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691.
Kim, Y. (2014). ?Convolutional neural networks for sentence classification?. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751.
King R. D., Sutton G. M. (2013). High times for hate crimes: Explaining the temporal clustering of hate-motivated offending. Criminology, 51 (4), 871?894.
Mikolov, T., Yih, W.-t., and Zweig, G. (2013a). ?Linguistic regularities in continuous space word representations?. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746-751.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc.
Nam, J., Kim, J., Loza Menc__a, E., Gurevych, I., and Furnkranz, J. (2014). ?Large-scale multi-label text classification ? revisiting neural networks?. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, volume 8725, pages 437-452.
Schieb C, Preuss M (2016), Governing Hate Speech by Means of Counter Speech on Facebook, 66th ICA Annual Conference, Fukuoka, Japan.
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ISCApad #248 |
| Tuesday, February 12, 2019 by Chris Wellekens |
6 Jobs
| 6-1 | (2018-08-03) Doctoral thesis at IJLRA (Sorbonne Université) Paris FranceDoctoral thesisNavigation aid for the visually impaired:
Laboratories IJLRA (Institut Jean le Rond d?Alembert, UMR 7190 CNRS ? Sorbonne Université) and IRCAM (Institut de Recherche et Coordination Acoustique/Musique, UMR 9912 STMS IRCAM ? CNRS ? Sorbonne Université) Doctoral school École Doctorale Sciences Mécaniques, Acoustique, Électronique et Robotique (SMAER): ED 391 Discipline Acoustics (Virtual Reality, Audio, Interaction, Aide Handicap) Co-supervision Brian KATZ (DR-CNRS, IJLRA) et Markus NOISTERNIG (CR, IRCAM) Keywords Virtual reality, 3D audio, spatial sound, spatial cognition, room acoustics, visual impairments, navigation aid
Research context This thesis project is placed in the context of the ANR 2018-2021 project RASPUTIN (Room Acoustic Simulations for Perceptually Realistic Uses in Real-Time Immersive and Navigation Experiences). In the domains of sound synthesis and virtual reality (VR), much effort had been placed on the quality and realism of sound source renderings, from text-to-speech to musical instruments to engine noise for use in driving and flight simulators. The same degree of effort cannot be seen with regards to the spatial aspects of sound synthesis and virtual reality, particularly with respect to the acoustics of the surrounding environment. Room acoustic simulation algorithms have for decades been improving in their ability to predict acoustic measurement metrics like reverberation time from geometrical acoustic models, at a cost of higher and higher computational requirements. However, it is only recently that the perceptual quality of these simulations are being explored beyond their musical applications. In real-time systems, where sound source, listener, and room architecture can vary in unpredicted ways, investigation of the perceptual quality or realism has been hindered by necessary simplifications to algorithms. This project aims to improve real-time simulation quality towards perceptual realism. The capability of a real-time acoustic simulation to provide meaningful information to a visually impaired user through a virtual reality exploration is the focus of the project. As a preparatory tool prior to visiting a public building or museum, the virtual exploration will improve user's knowledge of the space and navigation confidence during their on-site visit, as compared to traditional methods such as tactile maps. The thesis work entails participating in the creation and evaluation of a training system application for visually impaired individuals. Tasks involve the development of an experimental prototype in collaboration with project partners with a simplified user interface for the construction of virtual environments to explore. Working in conjunction with a selected user group panel who will remain engaged in the project for the duration, several test cases of interest will be identified for integration into the prototype and subsequent evaluations. The prototype will be developed by the thesis student in collaboration with Novelab (audio gaming) and IRCAM/STMS-CNRS (developers of the audio rendering engine). Design and evaluation will be carried out in collaboration with the Centre de Psychiatrie et Neurosciences and StreetLab/Institut de la Vision. The ability to communicate in French would be beneficial, but is not mandatory at the start of the project. Evaluations will involve different experimental protocols in order to assess the accuracy of the mental representation of the learned environments. From the point of view of the metrics relation preservation, participants will have to carry out experimental spatial memory tests as well as onsite navigation tasks.
Candidate profile: We are looking for dynamic, creative, and motivated candidates with scientific curiosity, strong problem solving skills, the ability to work both independently and in a team environment, and the desire to push their knowledge limits and areas of confidence to new domains. The candidate should have a Master in Computer Science, Acoustics, Architectural Acoustics, Multimodal Interfaces, or Audio Signal Processing. A strong interest in spatial audio, room acoustics, and working with the visually impaired is necessary. It is not expected that a candidate will have already all the skills necessary for this multidisciplinary subject, so a willingness and ability to rapidly step into new domains, including spatial cognition and psychoacoustics will be appreciated.
Domaine Réalité virtuelle, Audio, Interaction, Aide Handicap
Dates Preferred starting date from 1-Nov-2018 to 20-Dec-2019, and no later than March-2019.
Application Interested candidates should send a CV, transcript of Master?s degree courses, a cover letter (limit 2 pages) detailing their motivations for pursuing a PhD in general and specifically the project described above, and contact information for 2 references that the selection committee can contact. Incomplete candidatures will not be processed.
Application deadline Complete candidature files should be submitted to brian.katz@sorbonne-universite.fr and markus.noisternig@ircam.fr before 1-Oct-2018.
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| 6-2 | (2018-08-13) Post-docs at Idiap, Martigny, Switzerland Dear Colleagues,
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| 6-3 | (2018-08-17) 1 PhD position in affective computing at the Grenoble Alps University, France 1 PhD position in affective computing at the Grenoble Alps University Thesis description
================== The thesis targets three main objectives: 1) the development of a weakly-supervised learning methodology for the semi-automatic annotation of affective information from speech and text produced by humans while interacting with a chatbot 2) the development of a module that performs a robust fusion of inputs? representations (speech + text) in order to infer attributes of affect in varying noisy conditions 3) an evaluation of the system?s robustness in different contexts of interaction with the chat-bot. Recent advances in deep learning have shown promising results in many applications of affective computing [Picard-95], where ones of the most dominant tasks consist in quantifying attributes of human emotion, such as arousal, valence, or dominance [Russel-80], time-continuously from signals recorded by sensors [Wöllmer-08]. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) [Gers-99] have successfully been employed to model long-range contextual dependencies between attributes of affect and speech data [Eyben-10, Eyben-12, Ringeval-15], and convolutional neural networks (CNNs) have shown promising results for learning useful information from the raw signals when combined with LSTM-RNN in the so-called ?end-to-end? framework [Trigeorgis-16]. Recently, semi-supervised [Schmitt-16, Ghosh-16] and unsupervised [Cummins-18] methods of representation learning have shown the interest of exploiting resources from other domains in order to deal with the issue of data scarcity, which is of paramount importance for methods based on deep learning, as they need as many examples as possible to generalise well on expressions of affect produced ?in-the-wild? [Ringeval-18]. In this thesis, weakly-supervised methods based on deep learning will be exploited to perform semi-automatic annotation of human affective behaviour from speech and text ? either typed on a keyboard, or automatically retrieved from speech by an ASR. Context-aware novelty detection [Marki-15] based on deep LSTM auto-encoders will be used to detect novel affective content, and semi-supervised learning methods [Zhang-18] will be employed to enrich the model while following a curriculum learning [Lotfian-18]. Data exploited to build and evaluate the system will rely on the data collected during the project but also on existing publicly available datasets of emotion, including people with various culture, language, age, education, but also featuring different environments and contexts of interaction. Data automatically retrieved from social platforms like YouTube channels will be considered for automatically enriching the model in a ?virtuous circle? fashion. The envisioned starting date is December 2018. Requirements ============ We are looking for one candidate with a strong focus on deep learning for affective computing with the following profile: + Master?s degree with background in Machine Learning, Speech Processing, Affective Computing + Excellent programming skills (Python, Java, C/C++), knowledge of Keras/TensorFlow/Torch would be ideal + Ability to work independently and be self-motivated + Excellent communication skills in English Applying ======== To apply, please email your application to: fabien.ringeval@imag.fr, laurent.besacier@imag.fr, jean-philippe.vigne@atos.net and beatrice.bouchot@atos.net. The application should consist of a single pdf file including: + a curriculum vitae showing academic records with tracks related to the themes of the thesis + transcript of marks according to M1-M2 profile or last 3 years of engineering school + statement letter expressing your interest in the position and your profile relevance + contact and recommendation letter of at least one university referent Incomplete applications will not be processed. Potential candidates will be invited for an interview with the supervisors. Conditions of employment ======================== You will be hired on a fixed-term contract (3 years contract ? CIFRE) at ATOS, a global leader in digital transformation. Working at Grenoble (ATOS/LIG) ================================== You will be integrated in two teams with academic and industrial profiles: the GETALP team of the LIG, recognised for its research activities in the fields of speech and language processing, and the team Cognitive Intelligence from ATOS, who is specialised in Artificial Intelligence (AI) for the development of chatbots. ATOS is a leader in digital services with pro forma annual revenue of circa ? 12 billion and circa 100,000 employees in 73 countries, serving a global client base. ATOS R&D team has a very active innovation spirit backed by a culture of Intellectual Property. Together these have led to numerous disruptive developments, including more than 1,500 patents. ATOS Grenoble (1000 collaborators) is focusing on AI, working with a variety of clients to implement solutions where they create value. ATOS leadership in Cloud technology, Cybersecurity and High-performance computing, along with our partnerships with major AI companies (e.g., Google), help us provide clients with the resources, expertise and support they need. The LIG is one of the largest laboratories in Computer Science in France. It is structured as a Joint Research Center (Unité Mixte de Recherche) founded by the CNRS, the Grenoble Institute of Technology (Grenoble INP), the INRIA Grenoble Rhône-Alpes, and the Grenoble Alps University (UGA), which has recently been ranked as France?s number one university in eleven disciplines, including Computer Science & Engineering, in the latest Shanghai Academic Subject Rankings of World Universities 2017. The LIG hosts 17 research teams and three teams providing administrative and technical supports, which represent an overall of 450 collaborators including 205 permanent researchers, 143 PhD students, and 35 persons in supporting teams as identified in 2016. The city of Grenoble is located on a plateau at the foot of the French Alps and is advertised to be the ?Capital of the Alps? due to its immediate proximity to the mountains. The IMAG building hosting the LIG is located on a landscaped campus of 175 hectares, which straddles Saint-Martin-d?Hères and Gières, and welcome around 40,000 students and researchers working in various research institutions. Thanks to this campus, UGA has been ranked as the eighth most beautiful universities in Europe by the Times Higher Education magazine in 2018. Overall, Grenoble as a city is the largest research center in France after Paris with 22,800 researchers. References ========== [Cummins-18] Nicholas Cummins, Shahin Amiriparian, Gerhard Hagerer, Anton Batliner, Stefan Steidl, and Björn Schuller, An image-based deep spectrum feature representation for the recognition of emotional speech, in Proceedings of ACM MM 2017, pp. 478?484, October 2017, ACM. [Eyben-10] Florian Eyben, Martin Wöllmer, Alex Graves, Björn Schuller, Ellen Douglas-Cowie and Roddy Cowie, On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues, Journal on Multimodal User Interfaces 3(1-2):7?19, March 2010, Springer Nature. [Eyben-12] Florian Eyben, Martin Wöllmer, and Björn Schuller, A multi-task approach to continuous five-dimensional affect sensing in natural speech, ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments 2(1):6, March 2012, ACM. [Gers-99] Felix A. Gers, Jürgen Schmidhuber, and Fred Cummins, Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN 1999, pp. 850?855, ENNS. [Ghosh-16] Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency and Stefan Scherer, Representation learning for speech emotion recognition, in Proceedings of Interspeech 2016, pp. 3603?3607, September 2016, ISCA. [Lotfian-18] Reza Lotfian and Carlos Busso, Curriculum learning for speech emotion recognition from crowdsourced labels, arXiv:1805.10339, May 2018. [Marki-15] Erik Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency and Stefan Scherer, Representation learning for speech emotion recognition, in Proceedings of Interspeech 2016, pp. 3603?3607, September 2016, ISCA. [Picard-95] Rosalind W. Picard, Affective Computing, MIT Press. [Ringeval-15] Fabien Ringeval, Florian Eyben, Eleni Kroupi, Anil Yuce, Jean-Philippe Thiran, Touradj Ebrahimi, Denis Lalanne, and Björn Schuller, Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data, Pattern Recognition Letters, 66:25?30, November 2015, Elsevier. [Ringeval-18] Fabien Ringeval, Björn Schuller, Michel Valstar, Roddy Cowie, Heysem Kaya, Maximilian Schmitt, Shahin Amiriparian, Nicholas Cummins, Denis Lalanne, Adrien Michaud, Elvan Ciftçi, Hüseyin Güleç, Albert Ali Salah, and Maja Pantic, AVEC 2018 Workshop and challenge: Bipolar disorder and cross-cultural affect recognition, in Proceedings of AVEC?18, ACM MM, October 2018, ACM. [Russel-80] James A. Russel, A circumplex model of affect, Journal of personality and social psychology, 39(6):1161?1178, December 1980, APA. [Schimtt-16] Maximilian Schmitt, Fabien Ringeval, and Björn Schuller, At the border of acoustics and linguistics: Bag-of-Audio-Words for the recognition of emotions in speech. In Proceedings Interspeech 2016, pp. 495?499, San Fransisco (CA), USA, September 2016, ISCA. [Trigeorgis-16] George Trigeorgis, Fabien Ringeval, Raymond Brueckner, Erik Marchi, Mihalis Nicolaou, Björn Schuller and Stefanos Zafeiriou, Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network, in Proceedings ICASSP 2016, pp. 5200?5204, Shanghai, China, April 2016, IEEE. [Wöllmer-08] Martin Wöllmer, Florian Eyben, Stephan Reiter, Björn Schuller, Cate Cox, Ellen Douglas-Cowie, and Roddy Cowie, Abandoning emotion classes-towards continuous emotion recognition with modelling of long-range dependencies, in Proceedings of Interspeech 2008, Brisbane, Australia, pp. 597?600, ISCA. [Zhang-18] Zixing Zhang, Jin Han, Jun Deng, Xinzhou Xu, Fabien Ringeval, and Björn Schuller, Leveraging unlabelled data for emotion recognition with enhanced collaborative semi-supervised learning, IEEE Access, 6, April 2018, IEEE.
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| 6-4 | (2018-08-20) PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria. Subject: PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria. Job description:
The Medical University of Vienna (MUV), Austria, seeks to fill a position of a PhD-student within the project ?Objective differentiation of dysphonic voice quality types?. The candidate must hold a master?s degree, preferably in (one of) the fields of sound engineering, acoustical engineering, audio signal processing, or similar. The work will be conducted at the Division of Phoniatrics-Logopedics within the Department of Otorhinolaryngology of the MUV. The workgroup hosting the project is interested in the assessment of voice parameters relevant to the medical diagnosis and clinical care of voice disorders. A focus is given to functional assessment of voice, especially to the objective description of voice quality. The levels of description include kinematics of voice production, voice acoustics, and auditory perception of voice. Clinical studies are conducted with a laryngeal high-speed camera that records vocal fold vibration at 4000 frames per second. Microphone signals of the voice are recorded in parallel. Vibratory patterns of the vocal folds are analysed visually and computationally via modelling. Trajectories of vocal fold edges, spatial arrangements thereof, and glottal area waveforms are analysed. Regarding acoustics, analysis of audio recordings involves the implementation, testing, and training of specialized synthesizers for pathological voices. On the level of auditory perception, listening experiments are conducted, especially experiments involving discrimination tasks. Mandatory skills of the candidate are MATLAB programming, speech signal processing, psychoacoustics, good knowledge of English, good communication skills, and excellent analytical thinking. Optional skills of the candidate are knowledge of German, experience in a health care profession, image and video processing, Python, PureData, object-oriented programming, software engineering, version control (Subversion, Git, or similar), SQL, and XML. The project duration is 4-5 years. The Austrian Science Fund (FWF) budgets for doctoral candidates a gross salary of 2.112,40 Euro per month. Application documents can be submitted to philipp.aichinger@meduniwien.ac.at by October 31st, 2018. Interviews are planned for November 2018.The project is planned to start in December 2018. Information regarding the beautiful city of Vienna can be found at https://www.meduniwien.ac.at/web/en/international-affairs/living-in-vienna/.
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| 6-5 | (2018-08-27) POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique, Laboratoire national de métrologie et d'essais, Trappes, France POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique
Localisation : Laboratoire national de métrologie et d'essais, Trappes (78)
REF : ML/VOX/DE
CONTEXTE :
Le projet ANR VoxCrim (2017-2021) propose d’objectiver scientifiquement les possibilités de mise en œuvre d’une comparaison de voix dans le domaine criminalistique. Deux objectifs principaux : a) mettre en place une méthodologie permettant d’assurer l’efficacité et la compétence des laboratoires réalisant des comparaisons de voix, b) établir des standards de mesures objectives. Il est nécessaire que les outils et méthodologies utilisés dans la comparaison de voix soient évalués, et que leur utilisation soit effectuée dans un cadre explicable et transparent. Les actions menées dans le projet permettront ainsi de faciliter le traitement d’une comparaison de voix dans les services de police et permettront de renforcer la recevabilité de la preuve auprès des tribunaux. Le laboratoire national de métrologie et d’essais (LNE) apporte au projet son expertise en métrologie, normalisation, accréditation et comparaison inter-laboratoire, dans le but de constituer une solution méthodologique pratique permettant de rendre le processus de comparaison de voix transparent et explicable.
MISSIONS :
Les missions confiées s’organisent en trois tâches : - Spécifications du protocole de validation des méthodes de comparaison de voix, plus spécifiquement dans le domaine de la criminalistique. En s’appuyant sur l’existant en termes de normes et méthodologies de référence, le (la) post-doctorant(e) identifiera les besoins et possibilités pour la mise en place d’un protocole de référence. - Le (la) post-doctorant(e) vérifiera l’adéquation du protocole identifié avec les métriques de comparaison de voix identifiées par les chercheurs des laboratoires d’informatique et de phonétique associés au projet. Il (elle) s’assurera également de la compatibilité du protocole avec les méthodes de travail des centres scientifiques de la Police et de la Gendarmerie, membres du projet. - Il (elle) collaborera à l’organisation d’une comparaison inter-laboratoire s’appuyant sur ce protocole.
Le (la) post-doctorant(e) bénéficiera du soutien de différentes équipes du LNE dans la menée de ses travaux (équipes évaluation des systèmes de traitement de l’information, mathématiques-statistiques, et métrologie), et sera en interaction régulière avec les autres laboratoires et centres scientifiques membres du projet. Des publications (et présentations, le cas échéant) en conférences et journaux internationaux sont attendues du (de la) post-doctorant(e).
Bibliographie : Bonastre, J. F., Kahn, J., Rossato, S., & Ajili, M. (2015). Forensic speaker recognition: Mirages and reality. In Speech Production and Perception: Speaker-Specific Behavior. hal-01473992.
DUREE :
18 mois. Début en janvier 2019.
PROFIL :
Vous êtes titulaire d’un doctorat en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la parole. Vous possédez des connaissances en méthodologie d’évaluation et en biométrique vocale.
Pour candidater, merci d’envoyer votre CV et lettre de motivation à l’adresse recrut@lne.fr en rappelant la référence : ML/VOX/DE
==================================================== Agnes Delaborde, PhD Ingénieur de recherche en évaluation IA & robotique (Research engineer in AI and robotics evaluation) Direction des essais – DE536 agnes.delaborde@lne.fr Tél. : +33 (0)1 30 69 11 50 - Mob. : +33 (0)6 26 72 69 80
Laboratoire national de métrologie et d'essais 29 avenue Roger Hennequin 78197 Trappes Cedex - lne.fr
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| 6-6 | (2018-08-31) Post Doctoral Position (12 months), Natural Language Processing, INRIA-Loria, Nancy, FrancePost Doctoral Position (12 months), Natural Language Processing: ?Online hate speech against migrants?Keywords: hate speech, migrants, social media, natural language processing. Supervisors : Irina Illina and Dominique Fohr. The applicant will also collaborate with CREM Laboratory Start: end of 2018 ? begin of 2019 Location: INRIA-Loria, Nancy, France Duration: 1 year To apply: send the following documents to illina@loria.fr and dominique.fohr@loria.fr as soon as possible and no later than September 25th, 2018:
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| 6-7 | (2018-09-02) CDD IRISA, Rennes, France L'équipe Expression de l'IRISA ouvre un CDD de 24 mois sur le déploiement mobile d'un système de synthèse de la parole.
Mots-clés : synthèse de la parole, intelligence artificielle, machine learning, agents conversationnels.
Détails : - offre d'emploi : pièce jointe et ici : https://www-expression.irisa.fr/files/2018/09/fiche_de_poste.pdf - Équipe Expression : http://www-expression.irisa.fr/fr/ - IRISA : http://www.irisa.fr/ - Bac + 5 ou Bac + 2 avec compétence Android / iOS - Date limite de candidature : 5 octobre 2018
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| 6-8 | (2018-09-03) PhD position and Post-doc position: Privacy-respecting dialog systems,Saarland University,Germany PhD position and Post-doc position: Privacy-respecting dialog systems
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| 6-9 | (2018-09-08) Enseignant vacataire Université de Franche-Comté, Besançon, France Le département Sciences du Langage & FLE de l'Université de Franche-Comté (site de Besançon, SLHS) cherche un.e enseignant.e vacataire pour le TD de « Phonétique et multimodalité de la parole », en 2è année de licence Sciences du langage, au semestre 1, 12h par TD (2 voire 3), sur le créneau du vendredi 8-9h, 10-11h et/ou 13-14h.
Informations : ? Description : Ce cours s?intéresse à l?émergence de la parole des points de vue ontogénétique et phylogénétique. Il explore les modifications anatomiques et physiologiques qui rendent la parole possible. Le cours s?intéresse aussi aux différentes modalités (vocale, verbale, gestuelle) que les humains utilisent pour communiquer au quotidien.
? Objectifs pédagogique : Connaître l?importance de la parole dans la communication humaine, et les autres modalités qui la complètent
- Se familiariser avec les questions que posent les sciences humaines à propos de l?origine du langage. - S?initier d?un point de vue phonétique à l?unité et à la diversité des langues - Se familiariser avec les mécanismes de la phonation et approfondir les connaissances articulatoires du système phonétique français - Renforcer les compétences en transcription phonétique et s?initier à l?analyse acoustique de la parole. Le/la candidat.e devra : - soit être étudiant.e âgé.e de moins de vingt-huit ans au 1er septembre de l'année universitaire et inscrit.e.s en vue de la préparation d'un diplôme du troisième cycle, ou : - justifier d'une activité professionnelle principale d'une durée de 900 heures sur une période de 12 mois (décret du 16 septembre 2004). Contact : Sophie Mariani-Rousset (smariani@univ-fcomte.fr)
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| 6-10 | (2018-09-13) Senior Research and Development Engineer (m/f), ELDA, Paris The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context, acting as the distribution agency of the European Language Resources Association (ELRA), is currently seeking to fill an immediate vacancy for a Senior Research and Development Engineer position. ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.
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| 6-11 | (2018-09-22) Doctoral student (Speech Technology, Cognitive Science), Tampere University of Technology, Finland Doctoral student (Speech Technology, Cognitive Science), Tampere University of Technology, Finland
The new Tampere University and higher education community begin their operations on 1 January 2019. Tampere University of Technology, the University of Tampere and Tampere University of Applied Sciences are building a unique environment for multidisciplinary, inspirational and high-impact research and education and a hub of expertise in technology, health and society. Read more: https://www.tampere3.fi/en
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| 6-12 | (2018-09-17) Associate Linguist [Français]
Intitulé du poste :Associate Linguist [French] Champs linguistiques :Phonétique, Phonologie, Morphologie, Sémantique, Syntaxe, Lexicographie, TAL Lieu :Paris, France Description du poste :En tant qu’Associate Linguist, vous annoterez et réviserez des données linguistiques en français. L’Associate Linguist contribuera également à un certain nombre de tâches en traitement automatique des langues, dont :
Compétences requises:
Compétences désirées :
CV + lettre de motivation en Anglais : maroussia.houimli@adeccooutsourcing.fr 2730E brut/mensuel + 50% Pass Navigo + Mutuelle
Maroussia HOUIMLI Responsable recrutement Accueil en entreprise & Evénementiel et Marketing-Vente
T 06.24.61.08.43 E maroussia.houimli@adeccooutsourcing.fr
21 Boulevard Voltaire, 75011 - Paris
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| 6-13 | (2018-09-18) 3-year Postdoctoral Researcher in Multilingual Speech Processing, IRISA, Rennes, France (updated) 3-year Postdoctoral Researcher in Multilingual Speech Processing
The Expression research team focuses on expressiveness in human-centered data. In this context, the team has a strong activity in the field of speech processing, especially text-to-speech (TTS). This activity is denoted by regular publications in top international conferences and journals, exposing contributions in topics like machine learning (including deep learning), natural language processing, and speech processing. Team Expression takes part in multiple collaborative projects.Among those,the current position will take part in a large European H2020 project focusing on the social integration of migrants in Europe. Team’s website: https://www-expression.irisa.fr/ PROFILE Main tasks: 1. Design multilingual TTS models (acoustic models, grapheme-to-phoneme, prosody, text normalization…) 2. Take part in porting the team’s TTS system for embedded environments 3. Develop spoken language skill assessment methods Secondary tasks: 1. Collect speech data 2. Define use cases with the project partners Environment: The successful candidate will integrate a team of other researchers and engineers working on the same topics. Required qualification: PhD in computer science or signal processing Skills: ● Statistical machine learning and deep learning ● Speech processing and/or natural language processing µ● Strong object-oriented programming skills µ● Android and/or iOS programming are a strong plus CONTRACT Duration: 36 months, full time Salary: competitive, depending on the experience. Starting date: latest mid June 2019 APPLICATION & CONTACTS Send a cover letter, a resume, and references by email to: ● Arnaud Delhay,arnaud.delhay@irisa.fr ; ● Gwénolé Lecorvé, gwenole.lecorve@irisa.fr ; ● Damien Lolive, damien.lolive@irisa.fr. Application will be processed on a daily basis.
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| 6-14 | (2018-09-20) Inria is seeking a Technical Project Manager for a new European (H2020 ICT) collaborative project COMPRISE, INRIA, Nancy, France Inria is seeking a Technical Project Manager for a new European (H2020 ICT) collaborative
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| 6-15 | (2018-09-20) PhD grant at Universidad Politécnica de Madrid, Spain PhD grant at Universidad Politécnica de Madrid
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| 6-16 | (2018-08-20) PhD student opportunity at LTCI, Telecom ParisTech, Paris, France A PhD student opportunity is now available at LTCI, Telecom ParisTech, affective needs. Cohesion is considered as a highly valued group
property serving crucial roles for group effectiveness and performance. Scholars proposed theoretical models of cohesion having from one to five dimensions. Among these dimensions, the task and social ones were the most
investigated. The task dimension concerns the extent to which group members are united to achieve the group?s goals and objectives; the social dimension refers to the social relationships within the group (e.g. the extent to which group members like each other). The thesis will focus on the development of a computational model of cohesion among humans, able to integrate its task and social dimensions and also accounting for their relationship and their development over time. This work will be conducted in the framework of the ANR JCJC French national project GRACE (Groups? Analysis for automated Cohesion Estimation). Tasks: ****** - State-of-the-art on cohesion to identify which are its most suitable and frequent multimodal behavioral descriptors. State-of-the-art will span several research fields, including sociology, psychology, and computer science - Computation of multimodal behavioral descriptors of cohesion - Designing and performing experiments to collect a multimodal data set on cohesion - Designing, implementing, and evaluating a computational model of cohesion Profile: ******** The ideal candidate should have a strong academic background in one or more of the following fields: Computer Science, AI, Machine learning, Human- Computer Interaction, Information Technology, Affective Computing, Social Signal Processing, or closely related fields. In addition to a passion for science and programming, you should be open to extend your thinking to the issues linked to Human-Computer Interaction. Moreover, the ideal candidate should have: - Interest in multidisciplinary research at the interface between computer science and sociology/psychology - Excellent academic track record - Good command of English (written and spoken) - Strong programming skills (C++/Python) - Very good communication skills, commitment, independent working style as well as initiative and team spirit Offer: ****** Starting date: between winter 2018 and spring 2019. This is flexible and can be negotiated with the supervisor within the above-mentioned time frame. Application deadline: the evaluation of the PhD candidates starts immediately and it will continue until the position is filled. To apply please send by email to giovanna.varni@telecom-paristech.fr a single pdf file including: - A cover letter stating your research interests and how they could be related to the research topic the thesis focuses on. - A detailed CV - Transcripts of records of your MSc - List of at least 2 referees - Recommendation letters You are encouraged to contact Prof. Giovanna Varni for more information. Please quote ?PhD position? in the email subject for both asking information and application.
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| 6-17 | (2018-10-05) POST DOC 18 mois, Laboratoire national de métrologie et d'essais, Trappes (78), France
POST DOC 18 mois – Transparence et explicabilité de la comparaison de voix dans le domaine criminalistique
Localisation : Laboratoire national de métrologie et d'essais, Trappes (78)
REF : ML/VOX/DE
CONTEXTE :
Le projet ANR VoxCrim (2017-2021) propose d’objectiver scientifiquement les possibilités de mise en œuvre d’une comparaison de voix dans le domaine criminalistique. Deux objectifs principaux : a) mettre en place une méthodologie permettant d’assurer l’efficacité et la compétence des laboratoires réalisant des comparaisons de voix, b) établir des standards de mesures objectives. Il est nécessaire que les outils et méthodologies utilisés dans la comparaison de voix soient évalués, et que leur utilisation soit effectuée dans un cadre explicable et transparent. Les actions menées dans le projet permettront ainsi de faciliter le traitement d’une comparaison de voix dans les services de police et permettront de renforcer la recevabilité de la preuve auprès des tribunaux. Le laboratoire national de métrologie et d’essais (LNE) apporte au projet son expertise en métrologie, normalisation, accréditation et comparaison inter-laboratoire, dans le but de constituer une solution méthodologique pratique permettant de rendre le processus de comparaison de voix transparent et explicable.
MISSIONS :
Les missions confiées s’organisent en trois tâches : - Spécifications du protocole de validation des méthodes de comparaison de voix, plus spécifiquement dans le domaine de la criminalistique. En s’appuyant sur l’existant en termes de normes et méthodologies de référence, le (la) post-doctorant(e) identifiera les besoins et possibilités pour la mise en place d’un protocole de référence. - Le (la) post-doctorant(e) vérifiera l’adéquation du protocole identifié avec les métriques de comparaison de voix identifiées par les chercheurs des laboratoires d’informatique et de phonétique associés au projet. Il (elle) s’assurera également de la compatibilité du protocole avec les méthodes de travail des centres scientifiques de la Police et de la Gendarmerie, membres du projet. - Il (elle) collaborera à l’organisation d’une comparaison inter-laboratoire s’appuyant sur ce protocole.
Le (la) post-doctorant(e) bénéficiera du soutien de différentes équipes du LNE dans la menée de ses travaux (équipes évaluation des systèmes de traitement de l’information, mathématiques-statistiques, et métrologie), et sera en interaction régulière avec les autres laboratoires et centres scientifiques membres du projet. Des publications (et présentations, le cas échéant) en conférences et journaux internationaux sont attendues du (de la) post-doctorant(e).
Bibliographie : Bonastre, J. F., Kahn, J., Rossato, S., & Ajili, M. (2015). Forensic speaker recognition: Mirages and reality. In Speech Production and Perception: Speaker-Specific Behavior. hal-01473992.
DUREE :
18 mois. Début en janvier 2019.
PROFIL :
Vous êtes titulaire d’un doctorat en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la parole. Vous possédez des connaissances en méthodologie d’évaluation et en biométrique vocale.
Pour candidater, merci d’envoyer votre CV et lettre de motivation à l’adresse recrut@lne.fr en rappelant la référence : ML/VOX/DE
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| 6-18 | (2018-10-09) 2 permanent positions at the European Language resources Distribution Agency (ELDA), Paris, France The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill immediate vacancies for 2 permanent positions:
Both positions based in Paris. Please check the profile details for each open position here: http://www.elra.info/en/opportunities/ Contact: job@elda.org
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| 6-19 | (2018-10-08) Stage chez Airbus DS Elancourt, France Une offre de stage chez Airbus DS Elancourt sur la reconnaissance automatique de la parole :
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| 6-20 | (2018-10-09) Stage Master Recherche,L'intelligibilité chez des patients atteints de troubles de la parole, Avignon, France Stage Master Recherche – 6 mois Contact : Corinne Fredouille (corinne.fredouille@univ-avignon.fr)
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| 6-21 | (2018-10-09) Stage Master Recherche: L' évaluation des troubles de la parole dans la maladie de Parkinson, Avignon, France Stage Master Recherche – 6 mois Contact : Corinne Fredouille (corinne.fredouille@univ-avignon.fr)
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| 6-22 | (2018-10-12) Maitre de conf'érence, Centrale Supelec Metz France CentraleSupélec Metz recrute un MCF section 27 ou 61:
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| 6-23 | (2018-10-17) Tenure-Track Assistant Professor: Computational Linguistics, Rochester Institute of Technology, USA Job: Tenure-Track Assistant Professor: Computational Linguistics, Rochester Institute of Technology
The Department of English at the Rochester Institute of Technology invites applications for a full-time, 9 month tenure-track Assistant Professor of Computational Linguistics/Linguistics, beginning in August 2019. Candidates are expected to have an earned doctorate in Linguistics, Computer Science, or a related field by the time of appointment. The committee will consider candidates who will finish their doctorate degree in the first year.
RIT does not discriminate. RIT is an equal opportunity employer that promotes and values diversity, pluralism, and inclusion. For more information or inquiries, please visit RIT/TitleIX or the U.S. Department of Education at ED.Gov
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| 6-24 | (2018-10-19) post-doctoral researcher , University of Twente, The Netherlands In the context of the ZonMW project Smart Sports Exercises, the University of Twente is looking for a post-doctoral researcher to develop new multimodal, interactive, digitally enhanced training exercises on an instrumented floor with actuators (LED video screen) and sensors that offer minimized on-body motion sensing. The successful candidate will be embedded in the Human Media Interaction group, and will work together with a colleague already employed at the Biomedical Signals and Systems group who develops new models and methods for tracking and analyzing behavioral data of volleyball players.
As a successful applicant, you will be working in a highly interdisciplinary research project. The aim of the project is to enhance volleyball training and trainer education through three main lines of innovation: (1) development of new interactive concepts for digitally supported training exercises, using game like interaction on an instrumented LED video floor, (2) innovation of data science for automatic analysis of behavioural data in sports exercises to provide the necessary input, and (3) innovation in conceptual thinking about sport training and PE that combines technological and non-technological elements, incorporating pedagogy and training didactics. We target a range of users, from elite players to youth and recreational teams, and including players, trainers, and teacher educators.
The project is a collaboration between a consortium of research institutes, sports organisations, and companies. The University of Twente leads the project, and is responsible for the data science and interaction technology in the project. Other partners are the Windesheim University of Applied Sciences (CALO), InnoSportLab Sport & Beweeg, Sportservice Veenendaal, and LedGo BV.
In this particular open position, you will work on developing interactive training exercise systems and on evaluating user experience, perception and performance in explorative and experimental studies. This will be done iteratively, in close interaction with end users such as volleyball trainers and players.
Your profile
You are passionate to work one of the above topics, and have clearly relevant background for that. You hold a PhD degree in computer science, biomedical engineering, HCI, game design or other relevant domain. You are capable of designing and realizing interaction technology systems. An additional background in user centered research and design, games, or embodied interaction is considered a pre.
You are an independent and self-directed researcher and developer, but also a team player able to work in a diverse and multidisciplinary consortium. You are an excellent researcher as well as someone who can contribute to the realization of actual interactive technology systems.
More information on this position
How to applyApply to this position by submitting at the following link (closing date: October 31st): https://www.utwente.nl/en/organization/careers/vacancy/!/462306/ Your application should include the following documents:
Our offer
A challenging opportunity to perform post-doctoral research in the context of a highly ambitious 2-year ZonMw-funded project. The University of Twente offers a stimulating work environment in an area of applied, forefront research and offers strong and inspiring collaboration possibilities with the medical field. You will have a 0.8-1.0 fte position for the duration of ~2 years and can participate in all employee benefits the UT offers. The gross monthly salary starts with ? 3111,- in the first year and increases over time. Additionally, the University of Twente provides excellent facilities for professional and personal development, a holiday allowance of 8%, and an end of year bonus of 8.3%.
The organization
The University of Twente. We stand for life sciences and technology. High tech and human touch. Education and research that matter. New technology which leads change, innovation and progress in society. The University of Twente is the only campus university of the Netherlands; divided over five faculties we provide more than fifty educational programmes. We have a strong focus on personal development and talented researchers are given scope for carrying out groundbreaking research.
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| 6-25 | (2018-10-16) Postdoctoral ASR, Radbout University, Nijmegen, The Netherlands (updated) Radbout University, Nijmegen, The NetherlandsApplication deadline: 31 January 2019 Vacancy: Postdoctoral ASR ? Speech Researcher
on the project BLISS: Behaviour-based Language-Interactive Speaking Systems The Centre of Language and Speech Technology (CLST) at Radboud University Nijmegen has a vacancy for a post-doctoral speech technology researcher. BLISS [http://hstrik.ruhosting.nl/bliss/] combines research on how using Big Data and information extraction can make a substantial contribution to self-management and improvement of the individual health situation. In this project we will build a smart, personalized, spoken dialogue system (SDS) supporting self-management that is able to meaningfully communicate with people about issues regarding their personal health, well-being and happiness using Automatic Speech Recognition (ASR). For this SDS we need to personalise the ASR component to adapt to speaker variation and to topic changes in dialogue. As a researcher you will combine knowledge of Deep Neural Networks (DNNs, Kaldi) based ASR with experience in SDS. For more information: http://hstrik.ruhosting.nl/asr-postdoc-bliss/
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| 6-26 | (2018-10-16) Postdoctoral researcher, Radboud Univdersity, Nijmegen, The Netherlands Application deadline: 14 November 2018
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| 6-27 | (2018-10-16) Junior ASR Researcher, Radboud University, Nijmegen, The Netherlands Application deadline: 14 November 2018
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| 6-28 | (2018-10-18) Post-doc 2 years, Aalto University, Finland Aalto University, Department of Signal Processing and Acoustics (Finland) invites applications for
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| 6-29 | (2018-10-20) POSTDOCTORAL FELLOWSHIP , UNIVERSITY OF LORRAINE, Nancy, France
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| 6-30 | (2018-11-02) Funded PhD position, University of Glasgow, UK The University of Glasgow is seeking applications for the following funded PhD position in the area of Explainable AI, Planning, and Human-Machine Interaction. Interested students should apply as soon as possible
Eligibility: UK/EU students only Start date: 1 January 2019 Full details at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=98605
Many future industrial operations will be carried out by teams consisting of humans and machines. In this project, the student will investigate how human-machine trust and explainable/transparent artificial intelligence affect such human-machine collaborative tasks. The work will concentrate on the communication aspects: how the machine communicates its intentions and reasoning processes to the human, and how the human can query and interact with the robot’s plan.
The project will be driven by oilfield drilling applications, which involve control of complex equipment in a dynamic environment, with an increasing level of automation. In this setting, close coordination and trust between the human crew and the automation system is required: the crew must both understand why the machine acts the way it does, as well as be confident it has taken all available information into account.
The student should have excellent experience, enthusiasm and skills in the areas of artificial intelligence and/or automated planning and reasoning and/or natural language or multimodal interaction. Applicants must hold a good Bachelor’s or Master’s degree in a relevant discipline.
The project is an EPSRC iCASE award with Schlumberger Gould Research and it is expected that the student will spend some time working with the company in Cambridge. This will give you a great opportunity of working in an internationally excellent research group as well as a leading player in the oil and gas industry. Travel and accommodation costs will be paid during the time at Schlumberger.
For more information, please contact Dr Mary Ellen Foster MaryEllen.Foster@glasgow.ac.uk. Feel free to contact me directly if you have questions, but only applications received through the University of Glasgow (as linked from the FindAPHD page) can be considered officially.
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| 6-31 | (2018-11-04) Tenure positions at University of Santa Cruz, California, USA UNIVERSITY OF CALIFORNIA, SANTA CRUZ DEPARTMENT OF Computer Science and Engineering Assistant and Associate or Full Professor, Natural Language Processing
POSITION AVAILABLE July 1, 2019 (with academic year beginning September 2019). Degree must be in hand by June 30, 2019.
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| 6-32 | (2018-11-06) Stage au LIMSI, Orsay, France # Intitulé
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| 6-33 | (2018-11-05) PhD student ?Morphology in spoken word recognition models? Radboud University, Nijmegen, The Netherlands PhD student ?Morphology in spoken word recognition models?
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| 6-34 | (2018-11-11) Research engineer or post-doc position in Natural Language Processing at LORIA-INRIA, Nancy, France Research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system
Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS Team: Multispeech, LORIA-INRIA (https://team.inria.fr/multispeech/) Contact: illina@loria.fr, dominique.fohr@loria.fr Duration: 12-15 months Deadline to apply : December 20th, 2019 Required skills: Strong background in mathematics, machine learning (DNN), statistics, natural language processing and computer program skills (Perl, Python). Following profiles are welcome, either:
or
Excellent English writing and speaking skills are required in any case. Candidates should email a detailed CV with diploma
LORIA is the French acronym for the ?Lorraine Research Laboratory in Computer Science and its Applications? and is a research unit (UMR 7503), common to CNRS, the University of Lorraine and INRIA. This unit was officially created in 1997. Loria?s missions mainly deal with fundamental and applied research in computer sciences.
MULTISPEECH is a joint research team between the Université of Lorraine, Inria, and CNRS. Its research focuses on speech processing, with particular emphasis to multisource (source separation, robust speech recognition), multilingual (computer assisted language learning), and multimodal aspects (audiovisual synthesis).
Context and objectives
Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech. The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN. The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.
Main activities
? study and implementation of a noisy speech enhancement module and a propagation of uncertainty module; ? design a semantic analysis module; ? design a module taking into account the semantic and uncertainty information.
References
[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018. [Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017. [Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016. [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016. [Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016. [Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013.
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| 6-35 | (2018-11-13) Associate Linguist [Français] job offer, Paris, France Associate Linguist [Français] job offer
Intitulé du poste :Associate Linguist [French] Champs linguistiques :Phonétique, Phonologie, Morphologie, Sémantique, Syntaxe, Lexicographie, TAL Lieu :Paris, France Description du poste :En tant qu’Associate Linguist, vous annoterez et réviserez des données linguistiques en français. L’Associate Linguist contribuera également à un certain nombre de tâches en traitement automatique des langues, dont :
Compétences requises:
Compétences désirées :
CV + lettre de motivation en Anglais : maroussia.houimli@adeccooutsourcing.fr 2730E brut/mensuel + 50% Pass Navigo + Mutuelle
Maroussia HOUIMLI Responsable recrutement
E maroussia.houimli@adeccooutsourcing.fr
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| 6-36 | (2018-11-15) Student intership, LIG Lab, Grenoble, France Neural coreference resolution The internship may last from 4 up to 6 months, it will take place at LIG laboratory, GETALP team (http://lig-getalp.imag.fr/), starting from January/February 2019.
The student will be tutored by Marco Dinarelli (http://www.marcodinarelli.it) and Laurent Besacier (https://cv.archives-ouvertes.fr/laurent-besacier). Interested candidates must send a CV and a motivation letter to marco.dinarelli@ens.fr and laurent.besacier@univ-grenoble-alpes.fr. References: [1] Vincent Ng Supervised noun phrase coreference research: The first fifteen years. Proceedings of ACL, 2010 [2] Sam Wiseman, Alexander M. Rush, Stuart M. Shieber Learning Global Features for Coreference Resolution Proceedings of NAACL-HLT, 2016 [3] Kenton Leey, Luheng Hey, Mike Lewisz, and Luke Zettlemoyer End-to-end Neural Coreference Resolution Proceedings of EMNLP, 2017 [4] Kenton Lee Luheng He Luke Zettlemoyer Higher-order Coreference Resolution with Coarse-to-fine Inference Proceedings of NAACL, 2018 [5] Ilya Sutskever, Oriol Vinyals, Quoc V. Le Sequence to Sequence Learning with Neural Networks Proceedings of NIPS, 2014 [6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin Attention Is All You Need Proceedings of NIPS, 2017 [7] Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, Yuchen Zhang Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes Proceedings of EMNLP and CoNLL-Shared Task, 2012 [8] Zhang, Jiacheng, et al. 'Improving the Transformer Translation Model with Document-Level Context.? EMNLP 2018. ------------------------
Laurent Besacier
Professeur à l'Univ. Grenoble Alpes (UGA)
Laboratoire d'Informatique de Grenoble (LIG)
Membre Junior de l'Institut Universitaire de France (IUF 2012-2017)
Responsable équipe GETALP du LIG
Directeur de l'école doctorale (ED) MSTII
-------------------------
!! Nouvelles coordonnées !!: LIG
Laboratoire d'Informatique de GrenobleBâtiment IMAG 700 avenue Centrale Domaine Universitaire - 38401 St Martin d'Hères Pour tout contact concernant ED MSTII: passer par ed-mstii@univ-grenoble-alpes.fr
Nouveau tel: 0457421454
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| 6-37 | (2018-11-16) Post doc, U.R.I.-Octogone-Lordat, Universit de Toulouse, Toulouse, France
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| 6-38 | (2018-11-17 ) Language Resources Project Manager - Junior, at ELDA, Paris, France The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Language Resources Project Manager ? Junior position. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field. For further information about ELDA/ELRA, visit: http://www.elda.org
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| 6-39 | (2018-12-03) Post-doc at GIPSA-Lab, Grenoble, France Job offer: Automatic assessment of the fluency of young readers (H/F)
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| 6-40 | (2018-12-03) Post-doc at KTH, Stockholm, Sweden We are looking for a postdoc to conduct research in a multidisciplinary expedition project funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden?s largest individual research program, addressing compelling research topics that promise disruptive innovations in AI, autonomous systems and software for several years to come.
The project combines Formal Methods and Human-Robot Interaction with the goal of moving from conventional correct-by-design control with simple, static human models towards the synthesis of correct-by-design and socially acceptable controllers that consider complex human models based on empirical data. Two demonstrators, an autonomous driving scenario and a mobile robot navigation scenario in crowded social spaces, are planned to showcase the advances made in the project.
The focus of this position is on the development of data-driven models of human behavior that can be integrated with formal methods-based systems to better reflect real-world situations, as well as in the evaluation of the social acceptability of such systems.
The candidate will work under the supervision of Assistant Prof. Iolanda Leite (https://iolandaleite.com/) and in close collaboration with another postdoctoral researcher working in the field of formal synthesis.
This is a two-year position. The starting date is open for discussion, but ideally, we would like the selected candidate to start in April 2019.
QUALIFICATIONS
Candidates should have completed, or be near completion of, a Doctoral degree with a strong international publication record in areas such as (but not limited to) human-robot interaction, social robotics, multimodal perception, and artificial intelligence. Familiarity with formal methods, game theory, and control theory is an advantage.
Documented written and spoken English and programming skills are required. Experience with experimental design and statistical analysis is an important asset. Applicants must be strongly motivated, be able to work independently and possess good levels of cooperative and communicative abilities.
We look for candidates who are excited about being a part of a multidisciplinary team.
HOW TO APPLY
The application should include:
1. Curriculum vitae.
2. Transcripts from University/ University College.
3. A brief description of the candidate's research interests, including previous research and future goals (max 2 pages).
4. Contact of two references. We will contact the references only for selected candidates.
The application documents should be uploaded using the KTH's recruitment system: https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:239307/where:4/
The application deadline is ** January 15, 2019 **
ABOUT KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe?s leading technical and engineering universities, as well as a key center of intellectual talent and innovation. We are Sweden?s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
-----------------
Iolanda Leite Assistant Professor KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Department of Robotics, Perception and Learning (RPL) Teknikringen 33, 4th floor, room 3424, SE-100 44 Stockholm, Sweden Phone: +46-8 790 67 34 https://iolandaleite.com
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| 6-41 | (2018-12-04)PhD position at the University of Glasgow, Glasgow, UK The University of Glasgow is still seeking applications for the following funded PhD position in the area of Explainable AI, Planning, and Human-Machine Interaction. Interested students should apply as soon as possible
Eligibility: UK/EU students only Start date: 1 January 2019 (or as soon as possible thereafter) Full details at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=98605
Many future industrial operations will be carried out by teams consisting of humans and machines. In this project, the student will investigate how human-machine trust and explainable/transparent artificial intelligence affect such human-machine collaborative tasks. The work will concentrate on the communication aspects: how the machine communicates its intentions and reasoning processes to the human, and how the human can query and interact with the robot’s plan.
The project will be driven by oilfield drilling applications, which involve control of complex equipment in a dynamic environment, with an increasing level of automation. In this setting, close coordination and trust between the human crew and the automation system is required: the crew must both understand why the machine acts the way it does, as well as be confident it has taken all available information into account.
The student should have excellent experience, enthusiasm and skills in the areas of artificial intelligence and/or automated planning and reasoning and/or natural language or multimodal interaction. Applicants must hold a good Bachelor’s or Master’s degree in a relevant discipline.
The project is an EPSRC iCASE award with Schlumberger Gould Research and it is expected that the student will spend some time working with the company in Cambridge. This will give you a great opportunity of working in an internationally excellent research group as well as a leading player in the oil and gas industry. Travel and accommodation costs will be paid during the time at Schlumberger.
For more information, please contact Dr Mary Ellen Foster MaryEllen.Foster@glasgow.ac.uk. Feel free to contact me directly if you have questions, but only applications received through the University of Glasgow (as linked from the FindAPHD page) can be considered officially.
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| 6-42 | (2018-12-05 )Speech scientist at ReadSpeaker, Huis ter Heide, the Netherlands Speech scientist at ReadSpeaker, Huis ter Heide, the Netherlands
ReadSpeaker is looking for a full-time speech scientist to work on neural text-to-speech synthesis for a large variety of languages. The position to be filled is located at the office in Huis ter Heide, the Netherlands. It is very centrally located, close to the city of Utrecht. You will be part of an international team of speech- and DNN/ML-scientists working in the Netherlands, Sweden, the US, South Korea, and Japan.
Requirements ? Have a MSc or PhD in Computer Science, Electrical Engineering, or related discipline with specialization in speech synthesis, recognition, natural language processing, or machine learning.
? Solid Machine Learning background and familiar with standard speech and machine learning techniques (DNN, CNN, RNN, LSTM, Deep learning).
? Experience in building speech, natural language processing and/or machine learning systems
? Good skills in programming languages C/C++ and/or Python, and algorithm implementation
? Moderate experience in Software development
? Good written and spoken English communication skills
Contact information:
ReadSpeaker
Dolderseweg 2A 3712BP Huis ter Heide +31-(0)30-6924490 The Netherlands Esther Klabbers
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| 6-43 | (2018-12-05) Post-doc at GIPSA, Grenoble, France Le GIPSA cherche un(e) jeune docteur(e), spécialiste de reco automatique pour le projet e-FRAN Fluence pour l'évaluation de la fluence de jeunes lecteurs.
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| 6-44 | (2018-12-06) Positions at Qwant, Paris, France La section recherche de Qwant propose un stage et un poste permanent en Traitement Automatique des Langues dans le cadre applicatif de la Recherche d'Information, lié à des problématiques d'analyse de sentiments/d'opinion, traduction automatique, l'extraction d'information sémantique, agents conversationnels, etc. Ces postes sont liés à nos projets de recherche avec l'INRIA & européens H2020 dans lesquels ces problématiques seront explorées. Les postes à pourvoir seront situés à Paris, dans nos locaux près de la Porte Dauphine au 7 rue Spontini. L'équipe TAL est au cœur d'un écosystème dynamique de recherche applicatif composé, entre autres, des équipes de Traitement de l'Image, Apprentissage Automatique pour la Recherche d'Information, Protection de la vie Privée, et Cartographie. Les équipements comprennent notamment un cluster de calcul GPU dernier cri. Pour toute information complémentaire, vous pouvez me contacter directement à cette adresse : c.servan_CHEZ_qwant.com.
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| 6-45 | (2018-12-10) PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.
Subject: PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.Job description:
The Medical University of Vienna (MUV), Austria, seeks to fill a position of a PhD-student within the project ?Objective differentiation of dysphonic voice quality types?. The candidate must hold a master?s degree, preferably in (one of) the fields of sound engineering, acoustical engineering, audio signal processing, or similar. The work will be conducted at the Division of Phoniatrics-Logopedics within the Department of Otorhinolaryngology of the MUV. The workgroup hosting the project is interested in the assessment of voice parameters relevant to the medical diagnosis and clinical care of voice disorders. A focus is given to functional assessment of voice, especially to the objective description of voice quality. The levels of description include kinematics of voice production, voice acoustics, and auditory perception of voice. Clinical studies are conducted with a laryngeal high-speed camera that records vocal fold vibration at 4000 frames per second. Microphone signals of the voice are recorded in parallel. Vibratory patterns of the vocal folds are analysed visually and computationally via modelling. Trajectories of vocal fold edges, spatial arrangements thereof, and glottal area waveforms are analysed. Regarding acoustics, analysis of audio recordings involves the implementation, testing, and training of specialized synthesizers for pathological voices. On the level of auditory perception, listening experiments are conducted, especially experiments involving discrimination tasks. Mandatory skills of the candidate are MATLAB programming, speech signal processing, psychoacoustics, good knowledge of English, good communication skills, and excellent analytical thinking. Optional skills of the candidate are knowledge of German, experience in a health care profession, image and video processing, Python, PureData, object-oriented programming, software engineering, version control (Subversion, Git, or similar), SQL, and XML. The project duration is 4-5 years. The Austrian Science Fund (FWF) budgets for doctoral candidates a gross salary of 2.112,40 Euro per month. Application documents can be submitted to philipp.aichinger@meduniwien.ac.at by October 31st, 2018. Interviews are planned for November 2018.The project is planned to start in December 2018. Information regarding the beautiful city of Vienna can be found at https://www.meduniwien.ac.at/web/en/international-affairs/living-in-vienna/. -- MedUni Wien Signatur EN Univ.-Ass. DI Dr.techn. Philipp Aichinger Medical University of Vienna Währinger Gürtel 18-20, 1090 Vienna, Austria
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| 6-46 | (2018-12-12) Research and Development Engineer at ELDA Paris The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Research and Development Engineer. Under the supervision of the CEO, the responsibilities of the R&D Engineer include designing, developing, documenting, deploying and maintaining tools, software components or applications for Language Resource production and management.
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| 6-47 | (2018-12-12) Master R2 Internship, Loria, Nancy, FranceMaster R2 Internship in Natural Language Processing: Online hatespeech against migrants
Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS
Team: Multispeech, LORIA-INRIA
Contact: illina@loria.fr, dominique.fohr@loria.fr
Duration: 5-6 months
Deadline to apply : January 10th, 2018
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Motivations and context According to the 2017 International Migration Report, the number of international migrants worldwide has grown rapidly in recent years, reaching 258 million in 2017, among whom 78 million in Europe. A key reason for the difficulty of EU leaders to take a decisive and coherent approach to the refugee crisis has been the high level of public anxiety about immigration and asylum across Europe. There are at least three social factors underlying this attitude (Berri et al, 2015): the increase in the number and visibility of migrants; the economic crisis that has fed feelings of insecurity; the role of mass media. The last factor has a major influence on the political attitudes of the general public and the elite. Refugees and migrants tend to be framed negatively as a problem. This translates into a significant increase of hate speech towards migrants and minorities. The Internet seems to be a fertile ground for hate speech (Knobel, 2012). The goal of this master internship is to develop a methodology to automatically detect hate speech in social network data (Twitter, YouTube, Facebook). In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. Developments in Neural Network (Mikolov et al., 2013a) led to a renewed interest in the field of distributional semantics, more specifically in learning word embeddings (representation of words in a continuous space). Computational efficiency was one big factor which popularized word embeddings. The word embeddings capture syntactic as well as semantic properties of the words (Mikolov et al., 2013b). As a result, they outperformed several other word vector representations on different tasks (Baroni et al., 2014). Our methodology in the hate speech classification will be related on the recent approaches for text classification with Neural Networks and word embeddings. In this context, fully connected feed forward networks (Iyyer et al., 2015; Nam et al., 2014), Convolutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015) and also Recurrent/Recursive Neural Networks (RNN) (Dong et al., 2014) have been applied. On the one hand, the approaches based on CNN and RNN capture rich compositional information, and have outperformed the state-of-the-art results in text classification; on the other hand they are computationally intensive and require careful hyperparameter selection and/or regularization (Dai and Le, 2015).
Objectives
The goal of this Master internspeech Develop a new methodology to automatically detect hate speech, based on machine learning and Neural Networks. Human detection of this material is infeasible since the contents to be analyzed are huge. In recent years, research has been conducted to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods (Schmidt et al. 2017). Although current methods have reported promising results, their evaluations are largely biased towards detecting content that is non-hate, as opposed to detecting and classifying real hateful content (Zhang et al., 2018). Current machine learning methods use only certain task-specific features to model hate speech. We propose to develop an innovative approach to combine these pieces of information into a multi-feature approach so that the weaknesses of the individual features are compensated by the strengths of other features (explicit hate speech, implicit hate speech, contextual conditions affecting the prevalence of hate speech, etc.).
References
Baroni, M., Dinu, G., and Kruszewski, G. (2014). ?Don?t count, predict! a systematic comparison of context-counting vs. contextpredicting semantic vectors?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 238-247. Berri M, Garcia-Blanco I, Moore K (2015), Press coverage of the Refugee and Migrant Crisis in the EU: A Content Analysis of five European Countries, Report prepared for the United Nations High Commission for Refugees, Cardiff School of Journalism, Media and Cultural Studies. Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54. Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691. Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112. Kim, Y. (2014). ?Convolutional neural networks for sentence classification?. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751. Knobel M. (2012). L?Internet de la haine. Racistes, antisémites, néonazis, intégristes, islamistes, terroristes et homophobes à l?assaut du web. Paris: Berg International Mikolov, T., Yih, W.-t., and Zweig, G. (2013a). ?Linguistic regularities in continuous space word representations?. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746-751. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc. Nam, J., Kim, J., Loza Menc__a, E., Gurevych, I., and Furnkranz, J. (2014). ?Large-scale multi-label text classification ? revisiting neural networks?. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, volume 8725, pages 437-452. Schmidt A., Wiegand M.(2017). A Survey on Hate Speech Detection using Natural Language Processing, Workshop on Natural Language Processing for Social Media Zhang, Z., Luo, L (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
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| 6-48 | (2018-12-12) Research engineer or post-doc position, Loria, Nancy, FranceResearch engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition systemSupervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS
Team: Multispeech, LORIA-INRIA
Contact: illina@loria.fr, dominique.fohr@loria.fr
Duration: 12-15 months
Deadline to apply : January 20th, 2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech. The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN. The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.
Main activities
Skills Strong background in mathematics, machine learning (DNN), statistics Following profiles are welcome, either:
or
Excellent English writing and speaking skills are required in any case.
References
[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018. [Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017. [Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016. [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.
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| 6-49 | (2018-12-14) Master R2 internship at Loria, Nancy, France Master R2 internship in Natural Language Processing: Introduction of semantic information in a speech recognition system
Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS
Team: Multispeech, LORIA-INRIA
Contact: illina@loria.fr, dominique.fohr@loria.fr
Duration: 5-6 months
Deadline to apply : January 20th, 2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Motivations and context
Semantic and thematic spaces are vector spaces used for the representation of words, sentences or textual documents. The corresponding models and methods have a long history in the field of computational linguistics and natural language processing. Almost all models rely on the hypothesis of statistical semantics which states that: statistical schemes of appearance of words (context of a word) can be used to describe the underlying semantics. The most used method to learn these representations is to predict a word using the context in which this word appears [Mikolov et al., 2013b, Pennington et al., 2014], and this can be realized with neural networks. These representations have proved their effectiveness for a range of natural language processing tasks [Baroni et al., 2014]. In particular, Mikolov?s Skip-gram and CBOW models et al. [Mikolov et al., 2013b, Mikolov et al., 2013a] have become very popular because of their ability to process large amounts of unstructured text data with reduced computing costs. The efficiency and the semantic properties of these representations motivate us to explore these semantic representations for our speech recognition system. Robust automatic speech recognition (ASR) is always a very ambitious goal. Despite constant efforts and some dramatic advances, the ability of a machine to recognize the speech is still far from equaling that of the human being. Current ASR systems see their performance significantly decrease when the conditions under which they were trained and those in which which they are used differ. The causes of variability may be related to the acoustic environment, sound capture equipment, microphone change, etc. Objectives The speech recognition (ASR) stage will be supplemented by a semantic analysis to detect the words of the processed sentence that could have been misrecognized and to find words having similar pronunciation and matching better the context. For example, the sentence « Silvio Berlusconi, prince de Milan » can be recognized by the speech recognition system as : « Silvio Berlusconi, prince de mille ans ». Good semantic context representation of the sentence could help to find and correct this error. The Master internship will be devoted to the innovative study of the taking into account of semantics through predictive representations that capture the semantic features of words and their context. Research will be conducted on the combination of semantic information with information from denoising to improve speech recognition. As deep neural networks (DNNs) can model complex functions and get outstanding performance, they will be used in all our modeling. References [Deng, 2014] Deng, L. Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3-4), 197?387, 2014. [Goodfellow et al., 2016] Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press. http://www.deeplearningbook.org, 2016. [Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013. [Mikolov et al., 2013b] Mikolov, T., Sutskever, I., Chen, T. Corrado, G.S.,and Dean, J. Distributed representations of words and phrases and their compositionality, in Advances in Neural Information Processing Systems, 2013, pp. 3111?3119. [Pennington et al., 2014] Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543. [Povey et al, 2011] Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motl?cek, P., Qian, Y., Schwarz, Y., Silovsky, J., Stemmer, G., Vesely, K. The Kaldi Speech Recognition Toolkit, Proc. ASRU, 2011. [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016. [Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016.
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| 6-50 | (2018-12-18) Postdoc at IRIT, Toulouse, France L'équipe SAMoVA de l'IRIT (Institut de Recherche en Informatique de Toulouse) recrute un chercheur ou une chercheuse en post-doctorat pour le projet collaboratif LinTo (PIA - Programme d?Investissements d'Avenir), projet d?assistant conversationnel destiné à opérer en contexte professionnel pour proposer des services en lien avec le déroulement de réunions. Ce travail post-doctoral concerne l?analyse du flux audio pour extraire un ensemble d?indicateurs non verbaux destinés à compléter la transcription automatique générée par d?autres partenaires du projet. Cet enrichissement aura pour rôle d?apporter des indications précieuses pour aider à la compréhension du déroulement des réunions, que ce soit au niveau des interactions, entre participants ou avec l?assistant vocal, ou de manière plus détaillée au niveau du contenu des échanges. Plusieurs pistes de recherche pourront être explorées en fonction du profil de la personne recrutée ainsi des situations étudiées dans le cadre du projet : - Analyse acoustique pour le recherche de marqueurs prosodique pertinents ; - Exploration des approches de type Speech2Vect pour extraire des indicateurs plus marqués sémantiquement ; - Application de méthodes d'apprentissage semi-supervisé dans un contexte faiblement annoté. Informations Pratiques : Poste à pourvoir : post-doc Durée: 12-20 mois à partir de février 2019 Domaine : analyse acoustique - traitement automatique de la parole - apprentissage automatique - interaction conversationnelle Lieu : Institut de Recherche en Informatique de Toulouse (Université Paul Sabatier) - Equipe SAMoVA Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr) Dossier de candidature : à envoyer avant le 15 janvier 2019. Détail de l'offre : https://www.irit.fr/recherches/SAMOVA/pagejobs.html
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| 6-51 | (2018-12-18) Post Doctoral Research Associate, University of ESSEX, UK We are looking for a Post Doctoral Research Associate to collaborate at the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction taking into account the role of human chemosignals perception. This will involve also the development of robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, as well as the design and implementation of software for the execution of experiments with adaptive VR stimulation.
Application closing date 02/01/2019
Application Links
Job reference REQ02121
Job Details
The School of Computer Science and Electronic Engineering, the Department of Psychology, and the Essex Brain-Computer Interfaces and Neural Engineering Lab are pleased to announce this postdoctoral position in the Horizon 2020 project 'POTION: Promoting social interaction through emotional body odours?. The project will last five years and start in January 2019 and includes partners from the Universities of Pisa (Italy), Padova (Italy), and Essex (UK), the Universitat Politecnica De Valencia (Spain), the Katholieke Universiteit Leuven (Belgium), and the Karolinska Institutet (Sweden), and three companies ISPA CRL (Portougal), SRA Instruments (France) and Feel-Ing s.r.l. (Italy). POTION proposes a novel technological paradigm to delve deeper into understanding meaningful social interaction, combining new knowledge about the chemical composition of human social chemosignals together with a novel olfactory-based technology designed to drive social behaviour.
Duties of the Role
The Essex team's work on the project focuses on the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction. This models will be applied to evaluate socially relevant variables, such as trust, presence and inclusion as well as to generate optimal stimuli in artificially mediated social interactions. In particular, the models will cover the role of human chemosignal perception in social interactions. The models will be identified and tested using neurophysiological data (e.g. EEG), peripheral physiological activation (i.e., ECG, RESP, EDA) and behavioural changes (i.e., f-EMG) collected using VR scenarios of increasing complexity.
The successful applicant will research and develop Bayesian (DCM and Active Inference) computational models of multimodal social interaction with an emphasis on the role of human chemosignals. They will also develop robust algorithms for signal processing, statistical inference and extraction of information from EEG and other physiological signals, design and implement software for the execution of experiments with VR stimulation, and contribute to the reporting and dissemination of the project.
Skills and qualifications required
Applicants are expected to hold a PhD (or be close to completion) in Computational Neuroscience, Brain-computer Interfaces, Neural Engineering, Psychology, Machine Learning, Statistics, Physics, Mathematics, Computer Science or a closely related discipline, or equivalent professional experience or practice. The ideal candidate will have significant experience in computational modelling of social interaction, signal processing, statistical modelling of neural signals and processes, brain-computer interfaces, and virtual reality interfaces. Applicants are also expected to have a strong publication record (relative to their career stage) as first author, ideally including publications in 1st quartile journals in relevant areas.
We strongly encourage women to apply as they are currently under-represented in the School of Computer Science and Electronic Engineering.
At the University of Essex internationalism is central to who we are and what we do. We are committed to being a cosmopolitan, internationally-oriented university that is welcoming to staff and students from all countries and a university where you can find the world in one place.
Who we are
University of Essex has just been awarded the prestigious title of 'University of the Year' by the Times Higher Education for 'transforming the lives of a growing student body? and ' putting both staff and students first'
The School of Computer Science and Electronic Engineering (CSEE) at the University of Essex has an outstanding reputation for teaching and high-quality research in artificial intelligence, biomedical engineering, brain-computer interfaces, computer games, evolutionary computation, human language technology, robotics, networks and optoelectronics. Particularly relevant to this application is our research in artificial intelligence and in life and medical sciences applications, which was judged as world-leading in the recent Research Excellence Framework, the system for assessing the quality of research in UK higher education institutions. An important centre spanning both areas is the Essex Brain-Computer Interface and Neural Engineering (BCI-NE) Laboratory. The BCI lab was founded in 2004 by Dr Citi among others, and is one of the largest and best equipped in Europe. For over a decade, it has produced highly visible internationally leading research, with international collaborators at MIT, Berkley, the European Space Agency, and many others. Our members have led high-profile externally funded projects in the area of Assisted Living Technologies. Since 2007 our research has featured prominently in the UK?s Department of Health?s annual report on research and development work relating to assistive technology, e.g. see the 2012 report, which was presented to parliament.
The successful applicant will work in collaboration with the Department of Psychology at the University of Essex. This department was ranked 13th out of more than 100 in the UK in the most recent Research Excellence Framework (REF 2014), with 90% of our research rated as world-leading or internationally excellent. Academic staff in the department have wide-ranging and world-leading expertise in vision, cognition, cognitive neuroscience, health and social psychology. Students and staff make use of facilities in the Centre for Brain Science, a purpose-built facility dedicated to research and with considerable resources for understanding brain and behaviour.
The research facilities are located on our Colchester Campus, which is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester ? England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour. Home to over 13,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place. Colchester has a relatively low cost of living, while being well connected to London, the coast, and areas of natural beauty in East Anglia.
Please see the link below for a full job description and person specification which outlines the full duties, skills, qualifications and experience needed for this role plus more information relating to the post. We recommend you read this information carefully before making an application. Applications should be made on-line, but if you would like advice or help in making an application, or need information in a different format, please telephone the Resourcing Team (+44 1206 876559).
Feel free to contact us (Dr Citi, (CSEE) lciti@essex.ac.uk (PI), Dr Ognibene (CSEE) dimitri.ognibene@essex.ac.uk, Dr Foulsham (PSYCH) foulsham@essex.ac.uk) for an informal discussion about this post.
Job reference REQ02121
Application closing date02/01/2019
Application Links
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| 6-52 | (2018-12-20) Post-doc at Loria, Nancy L'équipe Multispeech recrute un post-doctorant sur l'articulation de l'allemand par des français Etude articulatoire des sons de l?allemand prononcés par des français Contexte L?équipe MULTISPEECH, de Inria Nancy-Grand Est, étudie différents aspects de la modélisation de la parole, comme la production, la reconnaissance et la synthèse de la parole. Dans le cadre du projet collaboratif e-FRAN METAL qui porte sur l?utilisation du numérique dans l?éducation, des techniques de la parole sont adaptées, enrichies, et mises en ?uvre pour aider à l?apprentissage d?une langue étrangère. L?objectif consiste alors à détecter les défauts de prononciation des apprenants (prononciation des sons et intonation), et à proposer des diagnostics pour aider l?apprenant à améliorer sa prononciation. Missions Dans ce cadre, une étude articulatoire portera sur la prononciation des sons de l?allemand par des locuteurs français. Plus précisément, une partie de l?étude concernera les consonnes fricatives qui ne sont pas présentes dans le système français, notamment les fricatives palatale (le [ç] de « ich ») et vélaire (le [x] de « Buch »). Afin d?analyser les interférences entre la langue première et la langue seconde sur le plan articulatoire, la prononciation des consonnes proches de celles étudiées, que ce soit dans le système natif des apprenants (le français) ou le système non natif (l?allemand) fera également partie de l?étude. Le travail commencera par une collecte de données articulatoires. Pour ce faire, le chercheur utilisera un articulographe qui fait partie d?une plateforme d?acquisition multimodale disponible au laboratoire d?accueil. L?analyse des sorties de l?articulographe se fera grâce au logiciel VisArtico qui a été développé au sein de l?équipe. Les données acoustiques, synchronisées avec les données articulatoires, seront recueillies et analysées. Enfin, une étude perceptive restreinte sera menée afin mettre en correspondance articulation et perception. L?étude doit permettre une meilleure compréhension des difficultés articulatoires rencontrées par les français parlant allemand en fonction des interférences entre les deux langues. Elle doit également permettre de présenter des stratégies pour prendre en compte ces difficultés afin d?améliorer la qualité des retours faits aux apprenants dans le domaine de l?apprentissage de l?oral d?une langue étrangère. Les résultats seront appliqués directement dans le projet e-FRAN METAL.
Prérequis : le candidat devra avoir soutenu sa thèse entre 2015 et 2018. Durée du poste à pourvoir : 18 mois, Début : dès que possible et au plus tard fin mars 2019 Lieu : INRIA Grand-Est, Nancy, France. Encadrants : Anne Bonneau (anne.bonneau@loria.fr) et Slim Ouni (slim.ouni@loria.fr) Profil et compétences recherchées : - Doctorat portant sur la phonétique ou le traitement de la parole (thèse soutenue thèse entre 2015 et 2018) - Bonnes connaissances en phonétique - Des connaissances de base en programmation (pouvoir écrire des scripts simples) - des bases en langues française et allemande.
Références bibliographiques. D Jouvet, A. Bonneau, J. Trouvain, F. Zimmerer, Y. Laprie, B. Moebius. 'Analysis of phone confusion matrices in a manually annotated French-German learner corpus?. Workshop on Speech and Language Technology in Education, Sep 2015, Leipzig, Germany. Proceedings SLaTE 2015, Workshop on Speech and Language Technology in Education. <hal-01184186> S. Ouni, L. Mangeonjean, I. Steiner. VisArtico: a visualization tool for articulatory data. 13th Annual Conference of the International Speech Communication Association ? InterSpeech 2012, Sep 2012, Portland, OR, United States. 2012. Wieling, M., Veenstra, P., Adank, P., and Tiede, M. (2015), 'Comparing L1 and L2 speakers using articulography' Proceedings of the International Congress of Phonetic Sciences (ICPhS).Glasgow.
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| 6-53 | (2018-12-21) Stage de fin d’études d’Ingénieur ou de Master 2, INA, Paris, France Étude et développement d’une solution de synthèse vocale pour l’imitation d’un locuteur
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| 6-54 | (2018-12-22) Postdoctoral researcher and doctoral student positions,IDIAP, Martigny, Switzerland Postdoctoral researcher and doctoral student positions in ubiquitous computing and
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| 6-55 | (2019-01-12) Research engineer or post-doc position in Natural Language Processing, LORIA-INRIA, Nancy, FranceResearch engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition systemSupervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS
Team: Multispeech, LORIA-INRIA
Contact: illina@loria.fr, dominique.fohr@loria.fr
Duration: 12-18 months
Deadline to apply : April 1th, 2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech. The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN. The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.
The recruited person will work in collaboration with an industrial partner.
Main activities
Skills Strong background in mathematics, machine learning (DNN), statistics Following profiles are welcome, either:
or
Excellent English writing and speaking skills are required in any case.
References [Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018. [Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017. [Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016. [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.
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| 6-56 | (2019-01-10) Postdoc in speech production (M/F), CNRS-Sorbonne, Paris, France
Postdoc in Speech Production (M/F)Reference : UMR7018-CECFOU-004 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). Skills Required skills - PhD in phonetics or on a subject related to speech production or speech motor control. Work Context The candidate will work closely with Cécile Fougeron and Leonardo Lancia. 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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| 6-57 | (2019-01-19) Several positions at the University of Naples Frederic II, Italy Job: Post-Doc Position
Start: Spring 2019
Duration: The grant will be for 18 months, renewable up to 6 (by mutual consent), with a yearly salary of ? 25.000,00
Topics: Adaptive Multimodal Human-Robot and Machine Interaction
Language requirement: English
Description:
The goal is to study and design adaptive multimodal interaction mechanism. In multimodal interaction, research focus is to rely on different modalities and investigate how to apply fusion techniques on these in order to generate the correct interpretation of the user intention. Here, we will investigate also how to select the proper communication channels and to optimize their features to each user. In this view, the majority of the robotic applications are based on static user models: this prevents systems from adapting independently and proactively to changes in the needs and preferences of users. The aim of the present proposal is to investigate how to merge human-robot and, in general, human-machine multimodal interaction research issues with online adaptive learning ones.
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Job: Research Assistant
Start: Spring 2019
Duration: The grant will be for 12 months, with a yearly salary from ? 15.000,00 up to 25.000,00 (depending on the experience and seniority of the candidate)
Topics: Social Signal Processing in Rehabilitation Domains
Language requirement: English
Description:
Social Signal Processing is the discipline concerned with the automatic analysis of human social behaviour and with the generation of coherent social signals in artificial embodied agents. The AVATEA project (Advanced Virtual Adaptive Technologies e-hEAlth) aims at providing an adaptive support to therapists and children during motor rehabilitation sessions so, in this domain, the proper recognition of social signals, such as attention, engagement and distress could provide a valuable index of therapeutic effectiveness. Formalising such measures will be the main topic of interest for candidates to this position, together with the appropriate research about methods to elicit social behaviour in children suffering from different, and possibly limiting, diseases.
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Job: Research Assistant
Start: Spring 2019
Duration: The grant will be for 12 months, with a yearly salary from ? 15.000,00 up to 25.000,00 (depending on the experience and seniority of the candidate)
Topics: Machine Learning for Profiling of Physical Capabilities
Language requirement: English
Description:
The AVATEA project (Advanced Virtual Adaptive Technologies e-hEAlth) aims at providing an adaptive support to therapists and children during motor rehabilitation sessions. To provide such support, it is necessary that the system is able to profile the physical capabilities of the patients and monitor his/her performance during the rehabilitation sessions by analyzing temporal data produced by different wearable sensors and sensors to be positioned on the instruments used for the exercises. Machine learning approaches supporting the creation of a user model will be investigated to address this problem.
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Job: Research Assistant
Start: Spring 2019
Duration: The grant will be for 12 months, with a yearly salary from ? 15.000,00 up to 25.000,00 (depending on the experience and seniority of the candidate)
Topics: Personalization in Human-Robot Interaction
Language requirement: English
Description:
The project PRIN UPA4SAR (User Profiling and Adaptation for Socially Assistive Robotics) goal is to design an adaptive behavior of a robotic system that is in charge of monitoring the user's Activity of Daily Living (ADL) in the case of people with dementia. In our opinion, the robot presence, in order to be effective and well accepted by users, must be the least invasive as possible. In fact, an interactive robotic device whose behavior is unrelated to the specific needs of a person, his/her abilities and preferences can cause discomfort. The majority of the robotic applications are based on static user models and on the specification of all the possible contexts of interaction. This makes such systems incapable of adapting independently and proactively to changes in the needs and preferences of users. In this direction, our goal is to design an adaptive behavior of the robotic system that is able to regulate its social interaction parameters (e.g., the interaction distances, proxemics, the speed of movements, and the same modality of interaction) on the basis of personality factors as well as of the cognitive state of the user.
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Applications are invited from candidates with Master Degree or a PhD in Cognitive Science, Robotics, Computer Science, Artificial Intelligence, Electronic Engineering or other relevant disciplines.
The selected candidate will join the PRISCA Laboratory (Projects of Intelligent Robotics and Advanced Cognitive Systems) in Naples. The PRISCA Lab is a dynamic, international, and multidisciplinary team that offers exciting scientific projects, as well as an excellent and stimulating research environment (http://prisca.unina.it/).
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How to apply
Closing Date: 15 February 2018
The selection will be based on CV and a skype interview.
Please send your CV and letters of recommendation to prof. Silvia Rossi (silrossi@unina.it)
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About Naples
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| 6-58 | (2019-01-05) Two postdoctoral researcher project /researcher positions in speech processing, University of Eastern Finland, Joenssu, Finland
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| 6-59 | (2019-01-14) PhD Thesis position in Natural Language Processing: Online hate speech against migrants, LORIA-INRIA, NancyPhD Thesis position in Natural Language Processing: Online hate speech against migrantsSupervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS Team: Multispeech, LORIA-INRIA Contact: illina@loria.fr, dominique.fohr@loria.fr Duration: 3 years Deadline to apply : April 1th, 2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python), neural networks tools. Candidates should email a detailed CV with diploma
Motivations and context
According to the 2017 International Migration Report, the number of international migrants worldwide has grown rapidly in recent years, reaching 258 million in 2017, among whom 78 million in Europe. A key reason for the difficulty of EU leaders to take a decisive and coherent approach to the refugee crisis has been the high level of public anxiety about immigration and asylum across Europe. There are at least three social factors underlying this attitude (Berri et al, 2015): the increase in the number and visibility of migrants; the economic crisis that has fed feelings of insecurity; the role of mass media. The last factor has a major influence on the political attitudes of the general public and the elite. Refugees and migrants tend to be framed negatively as a problem. This translates into a significant increase of hate speech towards migrants and minorities. The Internet seems to be a fertile ground for hate speech (Knobel, 2012). The goal of this PhD Thesis is to develop a methodology to automatically detect hate speech in social network data (Twitter, YouTube, Facebook). Our methodology in the hate speech classification will be related on the recent approaches for text classification with Neural Networks and word embeddings. In this context, fully connected feed forward networks (Iyyer et al., 2015; Nam et al., 2014), Convolutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015) and also Recurrent/Recursive Neural Networks (RNN) (Dong et al., 2014) have been applied. On the one hand, the approaches based on CNN and RNN capture rich compositional information, and have outperformed the state-of-the-art results in text classification; on the other hand they are computationally intensive and require careful hyperparameter selection and/or regularization (Dai and Le, 2015).
Objectives
The goal of this PhD Thesis is to develop a new methodology to automatically detect hate speech, based on machine learning and Neural Networks. Human detection of this material is infeasible since the contents to be analyzed are huge. In recent years, research has been conducted to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods (Schmidt et al. 2017). Although current methods have reported promising results, their evaluations are largely biased towards detecting content that is non-hate, as opposed to detecting and classifying real hateful content (Zhang et al., 2018). Current machine learning methods use only certain task-specific features to model hate speech. We propose to develop an innovative approach to combine these pieces of information into a multi-feature approach so that the weaknesses of the individual features are compensated by the strengths of other features (explicit hate speech, implicit hate speech, contextual conditions affecting the prevalence of hate speech, etc.).
The student will work in the framework of French-German project (ANR project).
References
Berri M, Garcia-Blanco I, Moore K (2015), Press coverage of the Refugee and Migrant Crisis in the EU: A Content Analysis of five European Countries, Report prepared for the United Nations High Commission for Refugees, Cardiff School of Journalism, Media and Cultural Studies. Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54. Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691. Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112. Knobel M. (2012). L?Internet de la haine. Racistes, antisémites, néonazis, intégristes, islamistes, terroristes et homophobes à l?assaut du web. Paris: Berg International Schmidt A., Wiegand M.(2017). A Survey on Hate Speech Detection using Natural Language Processing, Workshop on Natural Language Processing for Social Media Zhang, Z., Luo, L (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
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| 6-60 | (2019-01-15) Several postdoc openings at IDIAP, Martigny, Switzerland IDIAP has openings for at least one post-doc in the general area of speech and
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| 6-61 | (2019-01-17) Post-doctoral research position, Stellenbosch University, South Africa Post-doctoral research position: Extremely-low-resource radio browsing for humanitarian monitoring Stellenbosch University, South Africa
A post-doctoral research position focussing on the automatic identification of spoken keywords in multilingual environments with extremely few or even no resources using state-of-the-art architectures is available in the Digital Signal Processing Group of the Department of Electrical and Electronic Engineering at the University of Stellenbosch. This is part of an ongoing project to develop wordspotters that can be used to monitor community radio broadcasts in rural African regions as a source of early warning information during natural disasters, disease outbreaks, or other crises. This phase of the project will consider several languages spoken in Mali, at least some of which are severely under-resourced and have not been the subject of speech technology research before. Specific project objectives include the development of a research system, the development of deployable system, the development of new methods and techniques and the production of associated publishable outputs. The position is part of a collaborative project with the United Nations Global Pulse. References to papers already produced as part of the project are listed below, and some general further information is available at http://pulselabkampala.ug/. Applicants must hold a PhD (obtained within the last 5 years) in the field of Electronic/Electrical Engineering, Information Engineering, or Computer Science, or other relevant disciplines. Suitable candidates must have practical experience with automatic speech recognition systems in general and deep neural net architectures in particular, and should have an excellent background in statistical modelling and machine learning. The candidate must also have good programming skills and be able to use high level programming languages for developing prototype systems. Finally, candidates must have excellent English writing skills and have an explicit interest in scientific research and publication.
The position will be available for one year, with a possible extension to a second year, depending on progress and available funds.
Applications should include a covering letter, curriculum vitae, list of publications, research projects, conference participation and details of three contactable referees and should be sent as soon as possible to: Prof Thomas Niesler, Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602. Applications can also be sent by email to: trn@sun.ac.za. The successful applicant will be subject to University policies and procedures.
Interested applicants are welcome to contact me at the above e-mail address for further information regarding the project.
References:
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| 6-62 | (2019-01-24) Internship at LABRI, Talence, France Sujet de stage M2 : sleepiness detection and characterization in voice recordings.
- 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 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.
- 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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| 6-63 | (2019-01-24) Post-doctoral positions at Telecom-ParisTech, Paris, France Post-doctoral positions at Telecom-ParisTech on Deep learning approaches for social computing in human-agent interactions
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| 6-64 | (2019-01-25) Postdoc at IRIT, Toulouse, France L'équipe SAMoVA de l'IRIT (Institut de Recherche en Informatique de Toulouse) recrute un chercheur ou une chercheuse en post-doctorat pour le projet collaboratif LinTo (PIA - Programme d?Investissements d'Avenir), projet d?assistant conversationnel destiné à opérer en contexte professionnel pour proposer des services en lien avec le déroulement de réunions.
Ce travail post-doctoral concerne l?analyse du flux audio pour extraire un ensemble d?indicateurs non verbaux destinés à compléter la transcription automatique générée par d?autres partenaires du projet. Cet enrichissement aura pour rôle d?apporter des indications précieuses pour aider à la compréhension du déroulement des réunions, que ce soit au niveau des interactions, entre participants ou avec l?assistant vocal, ou de manière plus détaillée au niveau du contenu des échanges.
Informations Pratiques :
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| 6-65 | (2019-02-03) Senior speech scientist (acoustic modeling) at ELSA Corp. Lisbon, Portugal Senior speech scientist (acoustic modeling) at ELSA Corp. in Lisbon, Portugal or Remote
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| 6-66 | (2019-01-04) Lecturer position at LORIA and Mines Nancy, Nancy, France
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| 6-67 | (2019-01-06) Postdoc at the University of Colorado, Boulder, Co, USA 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.
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| 6-68 | (2019-02-01) Lecturer at LIMSI, Orsay, Paris, France Le département informatique de l?UFR Sciences de l?université
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| 6-69 | (2019-01-02) ASSOCIATE OR ASSISTANT PROFESSOR, Aalto University, Finland 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.
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| 6-70 | (2019-02-03) PhD and Postdoc positions at University of Genova, Italy Fully funded PhD and PostDoc positions are available at the Casa Paganini -
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| 6-71 | (2019-02-08) Fully funded PhD position at Graz University of Technology, Austria Fully funded PhD position at Graz University of Technology, Austria 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 March-April 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 the following information in PDF format to Prof. Gernot Kubin (g.kubin@ieee.org): 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. 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,
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