En tant que Project Manager, vous participerez au développement et à l’amélioration du traitement du langage naturel. Cela inclut :
Selon le type de projet, vous serez amené(e) à former, encadrer, et superviser une équipe afin de réaliser diverses tâches de traitement automatique des langues (TAL)
Le développement, la maintenance et l’amélioration de la qualité des systèmes de traitement du langage naturel
Entraîner et/ou évaluer des systèmes de dialogue ou de traitement du langage naturel, identifier les erreurs/régressions et proposer des solutions
La consultation linguistique sur le traitement automatique appliqué à une langue et/ou sur l’analyse/modélisation du dialogue
Garantir la qualité de l’output linguistique pour les utilisateurs dans les dialogues homme-machine
Compétences/Expériences :
Niveau natif en français canadien et maîtrise courante de l’anglais.
Diplôme de licence en linguistique, linguistique informatique et/ou dans des disciplines associées. (niveau bac + 4)
Capacité à comprendre rapidement des concepts techniques et à apprendre à manier les outils internes à une entreprise.
Réel intérêt pour les (nouvelles) technologies.
Réel intérêt pour le traitement automatique des langues, la linguistique théorique et descriptive ainsi que toutes les formes de ressources linguistiques.
Attention du détails, compétences d’organisation et de communication.
Expérience avec la gestion de projet.
Capacité à travailler efficacement de manière indépendante et avec flexibilité dans un environnement collaboratif évoluant constamment de façon rapide.
Intitulé du poste :
Project manager
Champs linguistiques :
Anglais, français, canadien (obligatoire)
Lieu :
Paris, France
Salaire : selon expérience
CV + lettre de motivation en Anglais : celine.couillaud@adeccooutsourcing.fr
The Expression team of the IRISA lab (France) is opening a 18-month postdoctoral position on speech synthesis and ?natural language processing for the Breton language, starting as soon as possible.
Detailed missions:
Development of NLP and TTS modules for Breton
Phonetization, grapheme-to-phoneme conversion
Text normalization
POS tagging and chunking
Adaptation of the team?s TTS engine.
This includes the use of machine learning techniques.
Profile / skills :
PhD in natural language processing, speech processing or machine learning
Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of developing the speech recognition architecture on our new platform, managing the expertise topics related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.
MISSIONS
Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:
Define, specify and implement the speech recognition architecture taking into account the limits and interests of both modes (embedded/deported)
Prototype and model the different dialogue platforms
Write the technical specifications of these HMIs
Study, analyze and respond to calls for tenders related to the subject
Participate in customer and supplier workshops
Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
Identify and document speech recognition technological innovations to extract added value for the company
Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
Collaborate within a dynamic and motivated team
Ensure that speech recognition works properly on the new platform
Create dialogue platforms that combine flexibility of use, robustness and performance
Provide expertise and support on the theme 'Speech Processing': advice, methods, tools
PROFILE
You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.
You have at least 5 years of experience in the field of speech recognition.
A good command of C++ and Python languages and a solid knowledge of speech processing are essential.
Ideally, you are familiar with:
Understanding natural language
Signal processing
Nuance Technologies
Google Assistant
Alexa
Java development under Android
Occasional travel is expected.
Fluent French is required.
Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.
Annonce pour le poste de Voice Assistant Integration Engineer (M/F) :
Would you like to be part of a French company known for its innovation and agility?
Come and join Parrot Faurecia Automotive, a leading automotive equipment manufacturer with a start-up mentality, working to develop the cockpit of the future!
Located in central Paris, our teams engineer innovative technologies by developing man-machine interfaces based on Android Auto to provide an intuitive connected experience to end users. Our embedded electronic solutions are equipped with powerful multi-core graphics processors to provide quick response times for multiple simultaneous instructions.
Join us in taking up one of the greatest challenges of the automotive industry!
We are looking for a Voice Assistant Integration Engineer (M/F)
CONTEXT
Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of managing the expertise subjects related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.
MISSIONS
Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:
Prototype and model the different dialogue platforms
Write the technical specifications of these HMIs
Study, analyze and respond to calls for tenders related to the subject
Participate in customer and supplier workshops
Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
Identify and document speech recognition technological innovations to extract added value for the company
Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
Collaborate within a dynamic and motivated team
PROFILE
You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.
You have at least 2 years of experience in the field of speech recognition.
You are proficient in C++ language, Java development under Android and you have a solid knowledge of Google and Alexa assistants.
Ideally, you are familiar with:
Understanding natural language
Speech processing
Signal processing
Nuance Technologies
The Python language
Occasional travel is expected.
Fluent French is required.
Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.
(2019-04-11) Project Manager Intellectual Property and Language Data , 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 an immediate vacancy for a permanent Project Manager Intellectual Property and Language Data position.
Under the CEO?s supervision, the Project Manager Intellectual Property and Language Data will handle legal issues related to compilation, use and distribution of language datasets on a European and international scale. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field.
His main tasks will consist of:
drafting and negotiating distribution contracts for language datasets to be added to an online catalogue;
analysing of the legal status of language datasets;
implementing GDPR requirements in the processing of language data;
supervising data collection, particularly in the context of public Open Data;
implementing evaluation procedures for IPR clearance of digital data.
A successful candidate:
holds a Master?s degree (or equivalent) in IT Law, with good understanding of intellectual property and data protection;
holds a Bachelor?s degree (or justifies equivalent experience) in Information Science, Knowledge Management or a similar domain;
speaks fluent English, with advanced writing and analytical skills;
is familiar with public licensing schemes (CC, GPL, etc.);
justifies experience in project management and/or participation in European or international projects;
is dynamic, communicative, flexible and willing to work on various tasks;
is capable of working independently as well as in a team;
is an EU citizen, or has a residence permit enabling him to work in France.
All applications will be carefully examined until the position is filled. The position is based in Paris.
Gross annual salary: 30.000-36.000 EUR depending on experience.
Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org.
ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.
Keywords: discriminative pattern mining, neural networks analysis, explainability of black box models, speech recognition.
Context:
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminative pattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year)post-doc with the following preferred skills: ? Some knowledge (interest) about speech recognition ? Knowledgeable in pattern mining (discriminative pattern mining is a plus) ? Knowledgeable in machine learning in general and deep learning particular ? Good programming skills in Python (for Keras and/or Tensor Flow) ? Very good English (understanding and writing)
However, good PhD applications will also be considered and, in this case, the position will last 3 years. The position will be funded by INRIA (https://www.inria.fr/en/). See the INRIA web site for the post-doc and PhD wages.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in June for an expected start in September 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015.
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
[8] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
[9] G. Saon, H.-K. J. Kuo, S. Rennie, M. Picheny: The IBM 2015 English conversational telephone speech recognition system?, Proc. Interspeech, pp. 3140-3144, 2015.
[10] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke : The Microsoft 2017 Conversational Speech Recognition System, IEEE ICASSP, 2018.
(2019-04-13) PhD Thesis position or research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system, LORIA, Nancy, France
PhD Thesis position or research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system
Duration of post-doc or research engineer: 12-18 months
Duration of PhD Thesis : 3 years
Deadline to apply : May 15th, 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.
Main activities
study and implementation of a noisy speech enhancement module and a propagation of uncertainty module;
design a semantic analysis module;
design a module taking into account the semantic and uncertainty information.
Skills
Strong background in mathematics, machine learning (DNN), statistics
Following profiles are welcome, either:
Strong background in signal processing
or
Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.
References
[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018.
[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc.IEEE Automatic Speech Recognition and Understanding Workshop, 2017.
[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACMTransactions 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.
[Peters et al., 2017] Matthew Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017. ?Semi-supervised sequence tagging with bidirectional language models.? In ACL.
[Peters et al., 2018] Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. ?Deep contextualized word representations?. In NAACL.
PhD Title: Remote and Automatic Monitoring of Bird Populations
Studentship: Full Scholarship, including fees (EU/Non EU) plus annual stipend of €18,000.
Start Date: Sept 2019
PhD Supervisor: Dr. Naomi Harte, Sigmedia Group, Electronic & Electrical Engineering, Trinity College Dublin, Ireland
Background:
The analysis of birdsong has increased in the speech processing community in the past 5 years. Much of the reported research has concentrated on the identification of bird species from their songs or calls. Birdlife International has identified over 12,000 sites globally that are home to bird species of conservation concern and other forms of biodiversity. Out of these, 422 are in danger due to a number of threats including human encroachment and climate change. One of the main challenges in properly managing these sites is adequately monitoring them to determine their state, threats to the ecosystem and responses to these threats. Remote monitoring is the best potential option to achieve the level of coverage required.
The objective of this PhD project is to define the next-generation approaches to the use of remote monitoring for populations of birds of conservation concern. This PhD programme will develop acoustic techniques for the monitoring of bird species of conservation concern by leveraging recent developments in speech and language processing technologies. The PhD will develop appropriate approaches to acoustic data collection in the wild to ensure that acoustic surveys yield accurate bird population data and investigate audio signal analysis steps necessary to extract useful information from these long recordings. In particular the student will focus on signal enhancement to mitigate noise, and the idea of diarisation, i.e. the concept of 'who sang when'. This ambitious approach will take concepts from speaker diarisation in the speech processing domain and attempt to make sense of recordings overall. Birdsong presents significant challenges beyond speech, with more rapid pitch fluctuations coupled with noisier recordings in the wild. Thus the research is very far from a re-application of knowledge from one domain to another. Also, rather than trying to identify specific species in a recording from a closed set of possible birds, this approach will consider an unconstrained set to add to the technical challenges and make the results even more impactful. The desire is to exploit online archives of birdsong recordings from experts such as those available on xeno-canto.org and The Macaulay Library at Cornell. Based on the known geographical location of a recording, spontaneous models of bird vocalisations from populations in that area could be constructed using machine learning performed on available archived recordings. Techniques developed originally for speaker
identification will be further developed for this application. This work will also leverage deep learning to quickly build accurate models from these large datasets.
Envisaged Outputs of the Research:
• Signal processing algorithms to address noise issues specific to remote recordings in bird habitats.
• Exploitation of advanced machine learning approaches, including deep learning, to identify portions of recordings that contain bird activity.
• Disruptive approaches to automatic bird species identification to leverage opensource repositories to identify birds present in the recordings.
Requirements:
The ideal candidate for this position will:
• Have a primary degree (first class honours) in Electrical Engineering, Computer Engineering or a closely related discipline.
• Possess strong written and oral communication skills in English.
• Have a strong background and interest in digital signal processing (DSP)
• Have strong coding skills
• Be mathematically minded, and be curious about nature.
Interested candidates should send an email to Prof. Naomi Harte at nharte@tcd.ie. The email MUST include the following:
• Candidate CV (max 2 pages)
• A short statement of motivation (half page)
• Scanned academic transcripts
• Proof of English language competency (where applicable, see 1)
• Name and contact details for TWO academic referees
(2019-04-20) Two PhD Positions in Computational Linguistics or Phonetics or Speech Science, Saarland University, Germany
Two PhD Positions in Computational Linguistics or Phonetics or Speech Science
We are pleased to announce the availability of two PhD positions in the Language Science and Technology department at Saarland University in a project funded by the German Research Foundation (DFG). The three-year project is entitled 'Pause-internal phonetic particles' and is directed by Jürgen Trouvain and Bernd Möbius. Its focus is on the production and perception of vocalisations such as breath noises and tongue clicks typically found in speech pauses. Research in the project will be based on corpus analyses and production and perception experiments and develop pause models for speech synthesis.
The successful candidates should have a Master's degree in Computational Linguistics or Phonetics or Speech Science, or a related discipline. A good command of English is mandatory. Working knowledge of German is desirable but not a prerequisite. Candidates must have completed their Master studies by the time of the appointment. We are happy to consider applicants who have not yet finished their MA/MSc by the time of application but will have submitted their thesis by the starting date. Both contracts are funded for three years at a 65% salary on the German TV-L 13 scale.
The doctoral researchers will join a vibrant community of speech and language researchers at Saarland University whose expertise spans areas such as computational linguistics, psycho-linguistics, language and speech technology, speech science, theoretical and corpus linguistics, computer science, and psychology. Saarland University offers a lively academic environment for phonetics research. The department of Language Science and Technology is one of the leading departments in speech and language in Europe, with approximately 50 postdoctoral researchers and PhD students. The flagship project at the moment is the Collaborative Research Centre on Information Density and Linguistic Encoding. It also runs a significant number of European and nationally funded projects.
Applicants with a degree in Phonetics, Computational Linguistics, Spoken Language Processing, Speech Technology, or related fields, are encouraged to submit a full CV (including transcripts and copies of certificates, as well as two references) and a sample of written academic work, accompanied by a cover letter, to trouvain[at]coli.uni-saarland.de and moebius[at]coli.uni-saarland.de. Review of applications will begin on June 3, 2019, and will continue until the suitable candidates are found.
Dr. Jürgen Trouvain and Prof. Bernd Möbius
-- Jürgen Trouvain Saarland University Language Science and Technology Campus C7.2 D-66123 Saarbrücken Tel.: +49 - (0)681 - 302 - 46 94
(2019-04-21) Technical Engineer/Scientist (Project Manager) position, ELDA, Paris
The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Technical Engineer/Scientist (Project Manager) position, specialized in Speech and Multimodal technologies.
Technical Engineer / Scientist (Project Manager) in Speech and Multimodal Technologies
Under the supervision of the CEO, the responsibilities of the Technical Engineer/Scientist include designing/specifying language resources, setting up production frameworks and platforms, carrying out quality control and assessment. He/she will be in charge of renovating the current language resources production workflows. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field. He/she will be in charge of conducting the activities related to language resources and Speech and Multimodal technologies. The task will mostly consist in managing language resources production projects and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the development team.
Profile :
PhD in computer science, speech, audiovisual/multimodal technologies
Experience and/or good knowledge in speech data collection, expertise in phonetics, transcription tools
Experience in speech recognition, synthesis, speaker ID and the well-used packages (e.g. KALDI) and the tools to produce, collect and assess quality of resources and datasets
Experience and/or good knowledge of the Language Technology area
Experience with technology transfer projects, industrial projects, collaborative projects within the European Commission or other international frameworks
Good knowledge of Linux and open source software
Proficiency in Python
Hands-on experience in Django is a plus
Ability to work independently and as part of a team, in particular the ability to supervise members of a multidisciplinary team
Dynamic and communicative, flexible to combine and work on different tasks
Proficiency in French and English
Citizenship of (or residency papers) a European Union country
All applications will be carefully examined until the position is filled. The position is based in Paris.
Salary: Commensurate with qualifications and experience.
Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org.
ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.
(2019-04-30) Ingénieur Développeur Domotique/Traitement multimédia temps-réel, LIG, Grenoble, France
Dans le cadre du projet national ANR VocADom, le Laboratoire d?Informatique de Grenoble (LIG) recrute un ingénieur Développeur Domotique/Traitement multimédia temps-réel. L?objectif général du projet de recherche industrielle VocADom vise à définir, en lien avec les utilisateurs finaux, les fonctionnalités d?un système domotique à commande vocale s?adaptant à l?utilisateur et utilisable au domicile dans des conditions réelles (bruit, présence de plusieurs personnes). Plus détails peuvent être trouvé sur le site du projet VocADom (https://vocadom.imag.fr).
*Mission :* La mission consiste à adapter le middleware OpenHAB gérant les capteurs et les actionneurs de l?appartement intelligent du LIG et d?un mini-kit domotique portable aux spécifications du projet et à réaliser l?interfaçage avec les outils de traitements automatiques développés par les partenaires du projet. Par ailleurs, l?ingénieur sera chargé de la mise en place technique des expérimentations et de leurs scénarios, incluant la production et la synchronisation des données multimédia (vidéo, audio, traces domotiques). Enfin, il sera soutien de l?intégration des algorithmes temps-réel des équipes de recherche dans l?architecture temps-réel développée par l?entreprise THEORIS, partenaire du projet. Le travail proposé comprendra les tâches suivantes : * prise en main et adaptation de l?infrastructure domotique OpenHab de l?appartement intelligent du LIG ; * conception du mini-réseau domotique portable (sur la base d?une architecture existante) ; * soutien aux expérimentations du projet ; * soutien à l?intégration des algorithmes temps-réels des équipes de recherche ; * documentation.
*Profil et compétences souhaitées :*
* niveau ingénieur ou M2 en informatique, * compétences opérationnelles en génie logiciel (gestion de version, tests, qualité de code), * connaissance de Java, OSGi, C, C++, Linux, * autonomie et force de proposition, capacité de gestion de projet, * une première expérience d?utilisation des middlewares domotiques (OpenHAB, UPnP, KNX...) serait un plus.
*Salaire :* 1650 à 1950e net/mois selon l?expérience
*Environnement de travail :* Le poste sera rattaché au Laboratoire d?Informatique de Grenoble, UMR CNRS, au sein de l?équipe GETALP. L?équipe GETALP (http://getalp.imag.fr/) regroupe plus de 40 chercheurs, ingénieurs et étudiants dans le domaine du traitement automatisé des langues et de la parole multilingue. Le candidat sera également amené à collaborer étroitement avec l?équipe IIHM du LIG et avec les collaborateurs de la Maison de l?Innovation et de la Création (Maci).
*Candidature* Envoyer un CV, une lettre de motivation accompagnée éventuellement de 1 à 3 lettres de recommandation à Michel.Vacher@imag.fr et Francois.Portet@imag.fr. Les candidatures seront examinées dès à présent au fil de l?eau jusqu?à la date de démarrage. Merci de candidater dès que possible avant cette date.
(2019-05-17) 2 PhDs in Trinity College Dublin, Ireland
2 PhDs in Trinity College Dublin, Ireland, to start in Sept 2019. Both come with a stipend of 18000Euros per year, along with full student fees for a 4 year period. Please contact me at nharte@tcd.ie if interested in either post.
Human Speech? How do I know it’s Real?
20 years ago, the major focus in developing speech synthesis systems was testing the intelligibility of the output speech. More recently, attention has switched focus to assessing not only intelligibility, but also naturalness, pleasantness, pauses, stress, intonation, emotion and listening effort. The intelligibility of systems is now so high, that synthetic voices are becoming more human-like. This is good news for generating realistic synthetic speech for applications such as voice reconstruction or gaming. In tandem, research in the area of speaker verification, or voice based biometrics, has started to pay closer attention to the issue of spoofing – where systems are attacked with reconstructed speech. Now, with improvements in speech synthesis, another realistic form of spoofing is the use synthetic speech generated by modelling the target user. So how can you tell when speech is real, or when it is fake? This is the focus of this PhD project and it goes to the very core of the nature of human speech.
Remote and Automatic Monitoring of Bird Populations
The objective of this PhD project is to define the next-generation approaches to the use of remote monitoring for populations of birds of conservation concern. This PhD programme will develop acoustic techniques for the monitoring of bird species of conservation concern by leveraging recent developments in speech and language processing technologies. The PhD will develop appropriate approaches to acoustic data collection in the wild to ensure that acoustic surveys yield accurate bird population data and investigate audio signal analysis steps necessary to extract useful information from these long recordings. Approaches will involve the use of signal processing and deep learning. The research will be conducted in collaboration with the Dept of Zoology at TCD.
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminative pattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year)post-doc with the following preferred skills:
? Some knowledge (interest) about speech recognition
? Knowledgeable in pattern mining (discriminative pattern mining is a plus)
? Knowledgeable in machine learning in general and deep learning particular
? Good programming skills in Python (for Keras and/or Tensor Flow)
? Very good English (understanding and writing)
However, good PhD applications will also be considered and, in this case, the position will last 3 years. The position will be funded by INRIA (https://www.inria.fr/en/). See the INRIA web site for the post-doc and PhD wages.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in June for an expected start in September 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
[8] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
[9] G. Saon, H.-K. J. Kuo, S. Rennie, M. Picheny: The IBM 2015 English conversational telephone speech recognition system?, Proc. Interspeech, pp. 3140-3144, 2015.
[10] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke : The Microsoft 2017 Conversational Speech Recognition System, IEEE ICASSP, 2018.
(2019-05-18)Tenure track assistant professor at Faculty of Medicine at the University of Toronto, Canada
The Department of Speech-Language Pathology, in the Faculty of Medicine at the University of Toronto, invites applications for a full-time tenure-stream appointment in the field of pediatric language disorders. The appointment will be at the rank of Assistant Professor and will commence on January 1, 2020 or shortly thereafter.
The successful candidate must have a Ph.D. in Speech-Language Pathology, Communication Disorders or a related field of research and expertise in child language and child language disorders, with a focus on semantics, syntax, pragmatics or literacy with a demonstrated record of excellence in research and teaching. Completion of a post-doctoral fellowship will be considered an asset, as will experience supervising student research and/or post-doctoral fellows. We require that candidates have a clinical background in speech-language pathology/communication disorders and are eligible for registration with the College of Audiologists and Speech-Language Pathologists of Ontario (CASLPO). We seek candidates whose research and teaching interests complement and strengthen our existing departmental strengths. The successful candidate will be expected to pursue innovative and independent research at the highest international level and to establish an outstanding, competitive and externally funded research program.
Research excellence should be demonstrated by high-quality peer-reviewed publications, peer-reviewed funding, the submitted research statement, presentations at significant conferences, awards and accolades, and strong endorsements from referees of high standing.
Evidence of excellence in teaching will be provided through teaching accomplishments and awards, the teaching dossier, including a detailed teaching statement, sample course syllabi, and teaching evaluations submitted as part of the application, as well as strong letters of reference.
Salary will be commensurate with qualifications and experience.
This is an exceptional opportunity to join the Department of Speech-Language Pathology at the University of Toronto, one of the most highly ranked research universities in North America. The department is housed in the Rehabilitation Sciences Building, which provides excellent teaching and research facilities. The University of Toronto offers unique opportunities for collaborative and interdisciplinary research, encourages innovative scholarship, and provides the prospect of teaching a diverse student population.
All qualified candidates are invited to apply by clicking the link below. Applicants must submit a cover letter, curriculum vitae, research statement (up to 3 pages), copies of up to three representative publications and a teaching dossier to include a detailed teaching statement (up to 3 pages), sample syllabi, and teaching evaluations.
Applicants must also arrange to have three letters of reference sent directly by the referee via email (on letterhead and signed) to search.slp@utoronto.ca by the closing date. If you have questions about this position, please contact search.slp@utoronto.ca. All application materials must be submitted online.
Submission guidelines can be found at http://uoft.me/how-to-apply. We recommend combining attached documents into one or two files in PDF format.
All application materials, including reference letters, must be received by July 18, 2019.
For more information about the Department of Speech-Language Pathology, please visit our home page at https://slp.utoronto.ca/.
The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ persons and others who may contribute to the further diversification of ideas.
As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.
All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.
(2019-05-19) PhD position at LPNC, Grenoble, France
SUBJECT TITLE: Bio-Bayes Predictions -- Coupling Biological and Bayesian Predictive Models in Neurocognitive Speech Processing RESEARCH FIELD: Computer science, Cognitive Science, Psychological sciences, Neurosciences SCIENTIFIC DEPARTMENT (LABORATORY?S NAME): LPNC -- Laboratoire de Psychologie et NeuroCognition DOCTORAL SCHOOL?S: MSTII -- Mathématiques, Sciences et technologies de l'information, Informatique SUPERVISOR?S NAME: Julien Diard, PhD
SUBJECT DESCRIPTION: The issue of the relationship between perception and production mechanisms is central to many domains in cognitive science. In speech communication for instance, where predictions from speech production simulation interact in various ways with perceptual processes. In this context, we have developed COSMO (Communicating about Objects using SensoriMotor Operations), a family of Bayesian algorithmic models of communicating agents. We have previously used such models to study the evolution of phonological systems (Moulin-Frier et al., 2015), speech perception and learning (Laurent et al., 2017; Barnaud et al., 2017; 2018), and speech production and adaptation (Patri et al., 2015; 2018).
However, so far, these models consider greatly simplified temporal dimensions. For instance, syllable perception was restricted to consonant-vowel syllables, assuming that the key points of speech trajectories, respectively at the center of the consonant and the vowel, were previously identified. This, of course, contrasts with natural speech processing, where sensory inputs and motor controls continuously unfold over time. Indeed, the neuronal substrates, in the brain, that deal with auditory input are well described in terms of their oscillatory nature, since they intrinsically have to deal with temporal properties of speech, and their predictive nature, since they aim at anticipating events.
In this PhD project, we aim at extending the COSMO framework to define the first Bayesian perceptuo-motor model of continuous speech communication. In previous work, in the domain of Bayesian visual word recognition modeling (Phénix et al., 2018; Ginestet et al., 2019), we have developed mathematical tools to describe the temporal dynamics of perceptual evidence accumulation across layers of hierarchical representations. Probability distributions at each layer (letters and words) evolve continuously over time, as a function of bottom-up sensory evidence and top-down lexical constraints, to predict upcoming events. Crucially, we have developed mathematical tools to model, on the one hand, attentional control of these information flows, and, on the other hand, asynchronous and asymmetric information transfer. Applying these mathematical constructs to speech communication modeling would yield a novel class of Bayesian hierarchical and predictive models, able to account for observations of neuronal oscillatory systems in the brain.
This PhD project is part of an international collaboration with Anne-Lise Giraud and Itsaso Olasagasti of the ?Auditory, Speech and Language Neuroscience? group at UNIGE (Université de Genève, Switzerland), with regular meetings and visits to Geneva planned. This international collaboration with Geneva will provide a unique framework for mixing the Bayesian approach with neuroscience constraints and data, providing a valuable multidisciplinary environment for the PhD project. The PhD candidate will integrate the ?Language? research team at LPNC (Laboratoire de Psychologie et NeuroCognition, Grenoble), will be supervised by Julien Diard (CR CNRS, LPNC) and Jean-Luc Schwartz (DR CNRS, Gipsa- Lab), and will be registered in the MSTII (Mathématiques, Sciences et technologies de l'information, Informatique) doctoral school of Univ. Grenoble Alpes.
ELIGIBILITY CRITERIA Applicants must hold a Master's degree (or be about to earn one) or have a university degree equivalent to a European Master's (5-year duration), Applicants will have to send an application letter in English and attach: - their last diploma; - their CV; - letters of recommendation are welcome. Address to send their application: Julien.Diard@univ-grenoble-alpes.fr
SELECTION PROCESS Application deadline: June, 21, 2019 at 17:00 (CET) Applications will be evaluated through a three-step process: 1. Eligibility check of applications before June, 24, 2019 2. 1st round of selection: the applications will be evaluated by a Review Board between June, 24, and June, 28, 2019. Results will be given June, 28, 2019. 3. 2nd round of selection: shortlisted candidates will be invited for an interview session in Grenoble on July, 5, 2019 (in Grenoble or by Skype).
TYPE of CONTRACT: temporary-3 years of doctoral contract JOB STATUS: Full time HOURS PER WEEK: 35 OFFER STARTING DATE: October, 1st, 2019 APPLICATION DEADLINE: June, 21, 2019 Salary: between 1768.55 ? and 2100 ? brut per month (depending on complementary activity or not) PROFILE SUMMARY: The candidate must have a background in computer science, applied mathematics or signal processing, with a strong affinity for cognitive sciences, or instead a background in cognitive science or computational neuroscience, with a strong affinity for mathematical modelling. This training must be validated, or be validated soon (before Summer 2019), by a Masters level diploma (or equivalent).
The candidate must have previous experience in scientific research in a laboratory. Mastery of a scientific calculus language, or of a general-purpose programming language is required (R, Python, Matlab, etc.). The candidate must also have a good command of scientific English, both spoken and written.
Previous experience in probabilistic modeling, dynamic systems modeling, or connectionist modeling are a plus but are not required. Previous knowledge of the field of perception modeling or speech production is also a plus.
(2019-05-20) Doctorant en robotique cognitive, Université de Grenoble, France
Lieu de travail : Grenoble
Date de publication : 1 Mai 2019
Noms des responsables scientifiques : Gérard Bailly (DR CNRS, GIPSA-Lab, U. Grenoble Alpes) et Pascal Huguet (DR CNRS, LAPSCO, U. Clermont Auvergne)
Type de contrat : CDD Doctorant/Contrat doctoral
Durée du contrat : 36 mois
Date de début de la thèse : 1 octobre 2019
Quotité de travail : Temps complet
Rémunération : 2 135,00 € brut mensuel
Sujet de la these:
Impact de la présence de robots attentifs et bienveillants sur les comportements et fonctionnements cognitifs humains
Contexte
La robotique sociale humanoïde vise à créer des robots capables d’interagir avec des partenaires humains à des fins de coopération dans des secteurs aussi variés que l’assistance aux personnes âgées, l’apprentissage de compétences par les enfants ou encore la cobotique dans l’industrie 4.0. L’objectif est souvent de substituer à un partenaire humain ou animal le robot dans des tâches où les propriétés d’endurance, de rapidité ou de flexibilité des attributions sociales de ces avatars sociaux sont bénéfiques. Si beaucoup d’études en interaction face-à-face montrent l’impact des comportements verbaux [1] et coverbaux [2] des robots sociaux sur ceux de leurs partenaires humains, peu d’études ont été consacrées aux effets comparés entre présence robotique vs. humaine en matière de surveillance de tâches dans lesquelles le robot ou son modèle humain ne sont pas directement engagés. Deux études récentes conduites au LAPSCO [3] [4] ont notamment permis de répliquer avec succès, sous condition de présence robotique, l’influence généralement bénéfique de la présence humaine en matière de contrôle de l’attention dans des tâches impliquant de réprimer un automatisme cognitif néfaste dans l’activité cible (e.g. [5]).
Sujet et plan de travail
Nous proposons ici d’explorer l’impact du comportement d’un robot humanoïde possédant des capacités de communication verbale et co-verbale étendues [6] sur les performances (en matière de contrôle cognitif) de sujets impliqués dans des tâches dans lesquelles le robot est directement impliqué ou plus simplement présent dans l’environnement d’interaction. Nous allons donc varier le degré avec lequel le robot s’immisce dans la tâche principale, afin de déterminer les signaux susceptibles d’optimiser l’expression des bénéfices attachés aux robots sociaux humanoïdes (et à terme, leur acceptabilité) et les signaux plus néfastes à éliminer.
Dans le cadre de cette thèse, nous explorerons l’impact du comportement robotique – dans un premier temps, téléopéré par un pilote humain [7] puis piloté par les modèles appris sur ces données comportementales [8] – sur le comportement observable des sujets (e.g., performances cognitives, signaux verbaux et co-verbaux, notamment regard), sur leurs signaux physiologiques (i.e. conduction dermale, rythme respiratoire et cardiaque) et sur leurs activités cérébrales (i.e. étude de l’onde négative d’erreur par EEG).
Retombées scientifiques et technologiques
Les retombées attendues de cette thèse sont multiples : d’abord des modèles de prédiction des liens entre comportements observables, variables physiologiques et activités cérébrales sous-jacentes, ceci en fonction des performances observées et des comportements actifs et réactifs du robot ; ensuite, un ensemble de stratégies de comportement de robots attentifs et bienveillants, adaptées aux profils psychologiques des sujets et aux indicateurs de performance propres à la tâche ; et enfin des protocoles d’évaluation automatique de ces profils psychologiques par des robots sociaux interactifs, susceptibles d’être déployés dans des applications en santé, éducation ou évaluation de ressources humaines.
Références
[1] K.-Y. Chin, Z.-W. Hong, and Y.-L. Chen, “Impact of using an educational robot-based learning system on students’ motivation in elementary education,” IEEE Transactions on learning technologies, vol. 7, no. 4, pp. 333–345, 2014.
[2] S. Andrist, X. Z. Tan, M. Gleicher, and B. Mutlu, “Conversational gaze aversion for humanlike robots,” presented at the Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, 2014, pp. 25–32.
[3] N. Spatola et al., “Improved Cognitive Control in Presence of Anthropomorphized Robots,” International Journal of Social Robotics, pp. 1–14, 2019.
[4] N. Spatola et al., “Not as bad as it seems: When the presence of a threatening humanoid robot improves human performance,” Science Robotics, vol. 3, no. 21, eaat5843, 2018.
[5] D. Sharma, R. Booth, R. Brown, and P. Huguet, “Exploring the temporal dynamics of social facilitation in the Stroop task,” Psychonomic bulletin & review, vol. 17, no. 1, pp. 52–58, 2010.
[6] Parmiggiani, Alberto, Elisei, Frédéric, Maggiali, Marco, Randazzo, Marco, Bailly, Gérard, and Metta, Giorgio, “Design and validation of a talking face for the iCub,” International Journal of Humanoid Robotics, vol. 12, no. 3, 20 pages, 2015.
[7] R. Cambuzat, F. Elisei, G. Bailly, O. Simonin, and A. Spalanzani, “Immersive teleoperation of the eye gaze of social robots,” in Int. Symposium on Robotics (ISR), Munich, Germany, 2018, pp. 232–239.
[8] Nguyen, Duc-Canh, Bailly, Gérard, and Elisei, Frédéric, “Learning off-line vs. on-line models of interactive multimodal behaviors with Recurrent Neural Networks,” Pattern Recognition Letters (PRL), pp. 29–36, 2017.
Au sein du département Parole & Cognition du GIPSA-Lab (Grenoble, France), l'équipe Cognitive Robotics, Interactive Systems & Speech Processing (CRISSP) développe des modèles de comportements multimodaux (parole, gestes, regard, etc.) pour des robots humanoïdes en interaction avec des partenaires humains. Elle s'appuie sur les moyens techniques du GIPSA-Lab et en particulier sur la plate-forme robotique MICAL qui gère le robot iCub NINA et les outils de développement.
Cette thèse s'inscrit dans le projet de l'équipe de développer des robots engagés dans des tâches finalisées nécessitant un contrôle fin de l’engagement. Elle est financée par le projet 80 ans du CNRS «Robotique Sociale Humanoïde et Cognition » (RSHC) impliquant des chercheurs de trois laboratoires : le LAPSCO et le LIMOS à Clermont et le GIPSA-Lab à Grenoble,
La thèse sera rattachée à l’école doctorale EEATS de Grenoble. Des déplacements et trois séjours de courte-durée à Clermont-Ferrand seront à prévoir pour collaborer avec les chercheurs du LAPSCO et du LIMOS de l’UCA.
Le candidat devra être titulaire d’un diplôme d’ingénieur et/ou d’un master en Sciences Cognitives ou en Robotique d’interaction. Le poste nécessite de solides connaissances en expérimentation, programmation et analyse statistique. Une formation initiale en neurosciences est un plus. Le candidat doit avoir de bonnes aptitudes de communication orale et écrite (français et anglais nécessaires) pour présenter aux congrès et rédiger des articles dans des revues scientifiques. Nous recherchons un jeune chercheur qui saura s’impliquer dans son projet, curieux, ayant une certaine autonomie et une forte motivation pour développer des compétences en synthèse et évaluation de comportements dans le domaine de l’interaction homme-robot. De plus, le candidat devra être apte à travailler en équipe sur des projets pluridisciplinaires.
Les candidatures devront inclure un CV détaillé ; au moins deux références (personnes susceptibles d’être contactées) ; une lettre de motivation d’une page ; un résumé d’une page du mémoire de master ; les notes de Master 1 ou 2 ou d’école d’ingénieur.
La date limite pour l’envoi des candidatures est le 15/7/2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Keywords: hate speech, social media, natural language processing.
The rapid development of the Internet and social networks has brought great benefits to women and men in their daily lives. Unfortunately, the dark side of these benefits has led to an increase in hate speech and terrorism as the most common and powerful threats on a global scale. Hate speech is a type of offensive communication mechanism that expresses an ideology of hatred often using stereotypes. Hate speech can target different societal characteristics such as gender, religion, race, disability, etc. Hate speech is the subject of different national and international legal frameworks. Hate speech is a type of terrorism and often follows a terrorist incident or event.
Social networks are incredibly popular today. Nowadays, Twitter, LinkedIn, Facebook and YouTube are used as a standard tool for communicating ideas, beliefs and feelings. Only a small percentage of people use part of the network for unhealthy activities such as hate speech and terrorism. But the impact of this low percentage of users is extremely damaging. For years, social media companies such as Twitter, Facebook and YouTube have invested hundreds of millions of dollars each year in the task of detecting, classifying and moderating hate. But these efforts are mainly based on manually revising the content to identify and remove offensive content, which is extremely expensive.
This thesis aims at designing automatic and evolving methods for the classification of hate speech in the field of social media. Despite the studies already published on this subject, the results show that the task remains very difficult. We will use semantic content analysis methodologies from automatic language processing (NLP) and methodologies based on deep learning (DNN) which is the revolution in the field of artificial intelligence. During this thesis, we will develop a research protocol to classify hate speech in the text in terms of hateful, aggressive, insulting, ironic, neutral, etc. character. This type of problem is placed in the context of the multi-label classification.
In addition, the problem of obfuscation of words in hate messages will need to be addressed. People who want to write hate speech on the Internet know that they risk being censored by rudimentary automatic systems of moderation. So, users try to obscure their words by changing the spelling or the spelling of words.
Among the crucial points of this thesis are the choice of the DNN architecture and the relevant representation of the data, ie the text of the internet message. The system designed will be validated on real flows of social networks.
Skills
Strong background in mathematics, machine learning (DNN), statistics
Following profiles are welcome, either:
Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.
References :
T Gröndahl, L Pajola, M Juuti, M Conti, N Asokan (2018) ?All You Need is? Love?: Evading Hate-speech Detection, arXiv preprint arXiv:1808.09115
Wiegand, M., Klakow, D. (2008). Optimizing Language Models for Polarity Classification. In Proceedings of ECIR, pp. 612-616.
Wiegand, M., Ruppenhofer, J. (2015). Opinion Holder and Target Extraction based on the Induction of Verbal Categories. In Proceedings of CoNLL, pp. 215-225.
Wiegand, M., Ruppenhofer J., Schmidt A., C. Greenberg (2018) Inducing a Lexicon of Abusive Words ? A Feature-Based Approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Wiegand, M., Wolf, M., Ruppenhofer, J. (2017) Negation Modeling for German Polarity Classification. In Proceedings of GSCL.
Zhang Z., Luo L. (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
(2019-06-07) PhD grant at ISIR and STMS, Paris, France
Modélisation multimodale de l’expressivité et de l’alignement pour l’interaction humain-machine
Directrice de thèse : Catherine Pelachaud (ISIR)
Co-encadrant : Nicolas Obin (STMS)
Contexte
Cette thèse s’inscrit dans un contexte particulièrement riche en développement d’interfaces de communication entre l’humain et la machine. Par exemple, l’émergence et la démocratisation des assistants personnels (smartphones, home assistants, chatbots) font de l’interaction avec la machine une réalité quotidienne pour de plus en plus d’individus. Cette pratique tend à s’amplifier et à se généraliser à un grand nombre d’usages et de pratique de l’être humain : depuis les agents d’accueil (aujourd’hui, quelques robots Pepper plus pour la démo que pour un usage réel), la consultation à distance, ou les agents embarqués dans les véhicules autonomes. L’expansion des usages appelle à une extension des modalités d’interaction et à l’amélioration de la qualité de l’interaction avec la machine : aujourd’hui, la voix constitue la modalité privilégiée de l’interaction, et les scénarios d’interaction demeurent très limités (demande d’information, question-réponse, pas de réelle interaction dans la durée). Les limitations principales sont d’une part une faible expressivité : le comportement de l’agent est encore souvent monomodal (voix, comme les assistants Alexa ou Google Home) et demeure très monotone, ce qui limite grandement l’acceptabilité, la durée et la qualité de l’interaction ; et d’autre part le comportement de l’agent est peu ou pas adapté à l’interlocuteur, ce qui diminue l’engagement de l’humain dans l’interaction. Lors d’une interaction humain-humain les phénomènes d’alignement (e.g., ton de voix, vitesse de mouvement corporel) sont des indices de compréhension commune et d’engagement dans l’interaction (Pickering et Garrod, 1999; Castellano et al, 2012). L’engagement est marqué par des comportements non-verbaux sociaux (nonverbal social behaviors) à des moments spécifiques de l’interaction : ce peut être des signaux de feedbacks (pour indiquer être en phase avec l’interactant), ou bien une forme d’imitation (par exemple : un sourire appelle un autre sourire, le ton de la voix reprend des éléments de celui de l’interactant), ou encore des signaux synchronisés avec ceux de l’interactant (la gestion des tours de parole). Cette thèse vise à modéliser le comportement de l’agent en fonction de celui de l’utilisateur pour qu’il puisse montrer son engagement attentionnel en vue de maintenir l’interaction et de rendre ses messages plus compréhensifs. L’adaptation du comportement de l’agent se produira à différents niveaux comportementaux (prosodique, lexicale, comportementale, imitation, tour de parole…). L’interaction humain-machine, avec un fort potentiel applicatif dans de nombreux domaines, est un exemple d'interdisciplinarité nécessaire entre humanités numériques, robotique, et intelligence artificielle.
Objectif
L’objectif de la thèse est de mieux comprendre et à modéliser les mécanismes qui régissent l’interaction multimodale (voix et geste) entre un humain et une machine, pour permettre de lever des verrous technologiques et permettre d’élaborer un agent conversationnel capable de s’adapter de manière naturelle et cohérente à un interactant humain.
1) Expressifs (Léon, 1993) : capable d’avoir une expression variée et cohérente pour maintenir l’attention de l’interlocuteur, souligner les points importants, améliorer la qualité de l’interaction et en allonger la durée (dépasser un ou deux tours de parole)
2) Alignés sur le comportement multimodal de l’interlocuteur (Pickering et Garrod, 1999; Castellano et al, 2012; Clavel et al, 2016) : c’est-à-dire capable d’adapter son comportement en fonction du comportement de l’interlocuteur, pour renforcer l’engagement de ce dernier dans l’interaction.
Dans un premier temps, la thèse proposera de réaliser une architecture neuronale unifiée pour la modélisation générative du comportement multimodale de l’agent. L’expressivité de l’agent, prosodique (Obin, 2011; Obin, 2015) et gestuelle (Pelachaud, 2009), sera modélisée par des architectures neuronales récurrentes aujourd'hui couramment utilisées pour la voix et le geste (Bahdanau et al, 2014, Wang, 2017, Robinson & Obin, 2019). La thèse se focalisera sur deux aspects essentiels de la modélisation du comportement de l’agent : le développement d’architectures structurées sur plusieurs échelles temporelles pour améliorer la modélisation de la variabilité prosodique et gestuelle à l’échelle de la phrase et à l’échelle du discours (Le Moine & Obin, 2019), et l’apprentissage d’un comportement multimodal cohérent par l’approfondissement de mécanismes d’attention multimodaux partagés appliqués à la synchronicité des profils prosodiques et gestuels générés (He, 2018).
Dans un deuxième temps, la thèse s’attaquera à l’alignement du comportement de l’agent avec celui de l’humain. La thèse approfondira particulièrement l’apprentissage interactif et par imitation pour adapter de manière cohérente le comportement multimodal de l’agent à l’humain (Weber, 2018; Mancini, 2019), à partir des bases de données de dialogues accessibles (telles que NoXi (récoltées à l’ISIR et annotées en terme d’engagement), IEMOCAP (USC, Carlos Busso), Gest-IS (Edinburgh University, Katya Saint-Amard)) pour apprendre la relation et aussi leur adaptation au cours de l'interaction entre les profils prosodiques et comportementaux des interlocuteurs.
La thèse sera co-encadrée par Catherine Pelachaud, de l’équipe PIRoS de l’ISIR, spécialisée en interaction humain-machine et agents conversationnels, et par Nicolas Obin, de l’équipe Analyse et Synthèse des Sons (AS) de STMS, spécialisée en modélisation générative de signaux de parole.. Le doctorant bénéficiera par ailleurs des connaissances, savoir-faire, et outils existants à STMS et à l’ISIR (par exemple : synthétiseur de parole ircamTTS développé à STMS, plateforme GRETA développée à l’ISIR) et de la logistique de calcul de STMS (serveur de calculs, GPU).
Bibliographie
(Bevacqua et al., 2012) Elisabetta Bevacqua, Etienne de Sevin, Sylwia Julia Hyniewska, Catherine Pelachaud, A listener model: Introducing personality traits, Journal on Multimodal User Interfaces, special issue Interacting ECAs, Elisabeth André, Marc Cavazza and Catherine Pelachaud (Guest Editors), July 2012, 6(1-2), pp 27-38.
(Castellano et al., 2012) G. Castellano, M. Mancini, C. Peters, P. W. McOwan. Expressive copying behavior for social agents: a perceptual analysis. IEEE Trans Syst, Man Cybern, Part A: Syst Hum 42(3), 2012.
(Clavel et al., 2016) Chloé Clavel, Angelo Cafaro, Sabrina Campano, and Catherine Pelachaud, Fostering user engagement in face-to-face human-agent interactions, in A. Esposito and L. Jain (Eds), Toward Robotic Socially Believable Behaving Systems - Volume I: Modeling Social Signals, Springer Series on Intelligent Systems Reference Library (ISRL), 2016
(Glas and Pelachaud, 2015) N. Glas, C. Pelachaud, Definitions of Engagement in Human-Agent Interaction, workshop ENHANCE, in International Conference on Affective Computing and Intelligent Interaction (ACII), 2015.
(Hall et al., 2005) L. Hall, S. Woods, R. Aylett, L. Newall, A. Paiva. Achieving empathic engagement through affective interaction with synthetic characters. Affective computing and intelligent interaction, 2005.
(He, 2018) Xiaodong He, Deep Attention Mechanism for Multimodal Intelligence: Perception, Reasoning, & Expression across Language & Vision, Microsoft Research, AI NEXTCon, 2018.
(Le Moine & Obin, 2019) Clément Lemoine, Modélisation neuronale de l’expressivité pour la transformation de la voix, stage de Master, 2019.
(Léon, 1993) P. Léon. Précis de phonostylistique : Parole et expressivité. Paris:Nathan, 1993.
(Obin, 2011) N. Obin. MeLos: Analysis and Modelling of Speech Prosody and Speaking Style, PhD. Thesis, Ircam-Upmc, 2011.
(Obin, 2015) N. Obin, C. Veaux, P. Lanchantin. Exploiting Alternatives for Text-To-Speech Synthesis: From Machine to Human, in Speech Prosody in Speech Synthesis: Modeling and generation of prosody for high quality and flexible speech synthesis. Chapter 3: Control of Prosody in Speech Synthesis, p.189-202, Springer Verlag, February, 2015.
(Ochs et al., 2008) M. Ochs, C. Pelachaud, D. Sadek, An Empathic Virtual Dialog Agent to Improve Human-Machine Interaction, Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Estoril Portugal, May 2008.
(Paiva et al., 2017) A. Paiva, I. Leite, H. Boukricha, Hana I. Wachsmuth 'Empathy in Virtual Agents and Robots: A Survey.', ACM Trans. Interact. Intell. Syst. (2017), 7 (3):11:1-11:40.
(Pelachaud, 2009) C. Pelachaud, Studies on Gesture Expressivity for a Virtual Agent, Speech Communication, special issue in honor of Björn Granstrom and Rolf Carlson, 51 (2009) 630-639.
(Poggi, 2007) I. Poggi. Mind, hands, face and body: a goal and belief view of multimodal communication. Weidler, Berlin, 2007.
(Robinson & Obin, 2019) C. Robinson, N. Obin, A. Roebel. Sequence-to-sequence modelling of F0 for speech emotion conversion, in IEEE International Conference on Audio, Signal, and Speech Processing (ICASSP), 2019.
(Sadoughi et al., 2017) Najmeh Sadoughi, Yang Liu, and Carlos Busso, 'Meaningful head movements driven by emotional synthetic speech,' Speech Communication, vol. 95, pp. 87-99, December 2017.
(Sidner and Dzikovska, 2002) C. L. Sidner, M. Dzikovska. Human-robot interaction: engagement between humans and robots for hosting activities. In: IEEE int conf on multimodal interfaces, 2002.
(Wang, 2017) Xin Wang, Shinji Takaki, Junichi Yamagishi. An RNN-Based Quantized F0 Model with Multi-Tier Feedback Links for Text-to-Speech Synthesis, Interspeech, 2017
(Wang, 2018) Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Fei Re, Ye Jia, Rif A. Saurous. « Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis », 2018.
(Weber, 2018) K. Weber, H. Ritschel, I. Aslan, F. Lingenfelser, E. André, How to Shape the Humor of a Robot - Social Behavior Adaptation Based on Reinforcement Learning, ACM International Conference on Multimodal Interaction, 2018.
(Mancini, 2019) M. Mancini, B. Biancardi, S. Dermouche, P. Lerner, C. Pelachaud, Managing Agent’s Impression Based on User’s Engagement Detection, Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, 2019.
(2019-06-14) PhD position: Privacy preserving and personalized transformations for speech recognition, INRIA Nancy and Univ.Le Mans, France
Thesis title
Privacy preserving and personalized transformations for speech recognition
This PhD thesis fits within the scope of a collaborative project (funded by the French National Research Agency) involving several French teams, among which, the MULTISPEECH team of Inria Nancy - Grand-Est and the LIUM (Laboratoire d'Informatique de l'Université du Mans).
This PhD position is in collaboration between the Multispeech team of the LORIA laboratory (Nancy) and Le Mans University. The thesis will be co-supervised by Denis Jouvet (https://members.loria.fr/DJouvet/) and Anthony Larcher (https://lium.univlemans.fr/team/anthony-larcher/). The selected candidate is expected to spend time in both teams over the course of the PhD.
Scientific Context
Over the last decade, great progress has been made in automatic speech recognition [Saon et al., 2017; Xiong et al., 2017]. This is due to the maturity of machine learning techniques (e.g., advanced forms of deep learning), to the availability of very large datasets, and to the increase in computational power. Consequently, the use of speech recognition is now spreading in many applications, such as virtual assistants (as for instance Apple’s Siri, Google Now, Microsoft’s Cortana, or Amazon’s Alexa) which collect, process and store personal speech data in centralized servers, raising serious concerns regarding the privacy of the data of their users. Embedded speech recognition frameworks have recently been introduced to address privacy issues during the recognition phase: in this case, a (pretrained) speech recognition model is shipped to the user's device so that the processing can be done locally without the user sharing its data. However, speech recognition technology still has limited performance in adverse conditions (e.g., noisy environments, reverberated speech, strong accents, etc.) and thus, there is a need for performance improvement. This can only be achieved by using large speech corpora that are representative of the actual users and of the various usage conditions. There is therefore a strong need to share speech data for improved training that is beneficial to all users, while preserving the privacy of the users, which means at least keeping the speaker identity and voice characteristics private1.
1 Note that when sharing data, users may want not to share data conveying private information at the linguistic level (e.g., phone number, person name, …). Such privacy aspects also need to be taken into account, but they are out-of-the scope of this thesis.
Missions: (objectives, approach, etc.)
Within this context, the objective of the proposed thesis is twofold. First, it aims at finding a privacy preserving transform of the speech data, and, second, it will investigate the use of additional personalized transforms, that can be applied on the user’s terminal, to increase speech recognition performance.
In the proposed approach, the device of each user will not share its raw speech data, but a privacy preserving transformation of the user speech data. In such approach, some private computations will be handled locally, while some cross-user computations may be carried out on a server using the transformed speech data, which protect the speaker identity and some of his/her features (gender, sentiment, emotions...). More specifically, this will rely on a representation learning to separate the features of the user data that can expose private information from generic ones useful for the task of interest, i.e., here, the recognition of the linguistic content. We will build upon ideas of Generative Adversarial Networks (GANs) for proposing such a privacy preserving transform. Since a few years, GANs are getting more and more used in deep learning. They
typically rely on both a generative network and a discriminative network, where the generator aims to output samples that the discriminator cannot distinguish from the true samples [Goodfellow et al., 2014; Creswell et al., 2018]. They have also been used as autoencoders [Makhzani et al., 2015] which are made of three mains blocks: encoder, generator and discriminator. In our case, the discriminators shall focus on discriminating between speakers and/or between voice-related classes (defined according to gender, emotions, etc). The training objective will be to maximize the speech recognition performance (using the privacy preserving transformed signal) while minimizing the available speaker or voice-related information measured by the discriminator.
As devices are getting more and more personal, it creates opportunities to make speech recognition more personalized. This includes two aspects: adapting the model parameters to the speaker (and to the device) and introducing personalized transforms to help hiding the speaker voice identity. Both aspects will be investigated. Voice conversion approaches provide example of transforms aiming at modifying the voice of a speaker so that it sounds like the voice of another target speaker [e.g., Chen et al., 2014; Mohammadi & Kain, 2014]. Similar approaches can thus be applied to map speaker specific features to those of a standard (or average) speaker, which thus would help concealing its identity. To take benefit of the increased personal usage of terminals, speaker and environment specific adaptation will be investigated to improve speech recognition performance. Collaborative learning mixing speech and speaker recognition has been shown to benefit both tasks [Liu et al. 2018; Garimella et al. 2015] and provide a way to combine both information in a single framework. This approach will be compared to adaptation of deep neural networks-based models [e.g., Abdel-Hamid & Jiang, 2013] to handle best different amounts of adaptation data.
Skills and profile:
Master in machine learning or in computer science
Background in statistics, and in deep learning
Experience with deep learning tools is a plus
Good computer skills (preferably in Python)
Experience in speech and/or speaker recognition is a plus
Bibliography:
[Abdel-Hamid & Jiang, 2013] Abdel-Hamid, O., & Jiang, H. Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code. In ICASSP-2013, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7942-7946, 2013.
[Chen et al., 2014] Chen, L. H., Ling, Z. H., Liu, L. J., & Dai, L. R. Voice conversion using deep neural networks with layer-wise generative training. TASLP-2014, IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(12), pp. 1859-1872, 2014.
[Creswell et al., 2018] Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., and Bharath, A. A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine 35, 1, 53-65, 2018.
[Garimella et al. 2015] Garimella, S., Mandal, A., Strom, N., Hoffmeister, B., Matsoukas, S., & Parthasarathi, S. H. K., Robust i-vector based adaptation of DNN acoustic model for speech recognition. In INTERSPEECH, 2015.
[Goodfellow et al., 2014] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In Advances in neural information processing systems, pp. 2672-2680, 2014.
[Liu et al. 2018] Y. Liu, L. He, J. Liu, and M. Johnson, Speaker Embedding Extraction with Phonetic Information,' in INTERSPEECH , pp. 2247-2251, 2018
[Makhzani, 2015] Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.
[Mohammadi & Kain, 2014] Mohammadi, S. H., & Kain, A. Voice conversion using deep neural networks with speaker-independent pre-training. In SLT-2014, Spoken Language Technology Workshop , pp. 19-23, 2014.
[Saon et al., 2017] G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim, B. Roomi, and P. Hall. English conversational telephone speech recognition by humans and machines. Technical report, arXiv:1703.02136, 2017.
[Xiong et al., 2017] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig. Achieving human parity in conversational speech recognition. Technical report, arXiv:1610.05256, 2017.
The candidates are required to provide the following documents in a single pdf or ZIP file:
CV
A cover/motivation letter describing their interest in the topic
Degree certificates and transcripts for Bachelor and Master (or the last 5 years)
Master thesis (or equivalent) if it is already completed, or a description of the work in progress, otherwise
The publications (or web links) of the candidate, if any (it is not expected that they have any)
In addition, one recommendation letter from the person who supervises(d) the Master thesis (or research project or internship) should be sent directly by his/her author to the prospective PhD advisor.
(2019-06-16) PhD position: Hybrid Bayesian and deep neural modeling for weakly supervised learning of sensory-motor speech representations, University of Grenoble-Alpes, France
Open fully-funded PhD position: “Hybrid Bayesian and deep neural modeling for weakly supervised
learning of sensory-motor speech representations”
The Deep-COSMO project, part of the new AI institute in Grenoble, is welcoming applications for a 3-year, fully funded
PhD scholarship starting October, 1st, 2019 at GIPSA-lab (Grenoble, France)
TOPIC: Representation learning, speech production and perception, Bayesian cognitive models, generative neural
(2019-06-20) Post-doc position, CNRS and Unv.Aix-Marseille, Aix-en-Provence, France
POST-DOC POSITION (18 months) - Forensic Voice Comparison (VoxCrim project): ability, limitations and specificities of listeners in speaker identification tasks Laboratoire Parole et Langage (CNRS and Aix-Marseille Université) ? Aix-en-Provence, France
CONTEXT The post-doc will be carried out within the framework of the VoxCrim project, funded by an ANR (Agence nationale de la recherche) grant (2017-2021, https://anr.fr/Project-ANR-17-CE39-0016). VoxCrim focuses on national security and legal/justice applications and aims to provide a validated scientific objective framework for all types of forensic voice comparison methods (automatic and phonetic). The goal is to develop certified standards to determine the specific areas in which voice comparison methods are applicable. The project includes two complementary subject areas: 1. the proposal of methodological standards to homogenize the expertise of voice comparison in a judicial environment, 2. the development of basic research in the fields of automatic speech processing and phonetics (speaker characteristics in the production and perception of speech). The post-doc will participate in the second subject area (speaker characteristics in the production and perception of speech). Two questions need to be answered: What are the abilities and limits of listeners in speaker identification tasks? Which cues do listeners use to identify speakers?
TASKS The main objective will be to conduct perception experiments aimed at assessing the ability of listeners in several speaker identification tasks. The post-doctoral fellow will: - design experimental protocols - create and manipulate acoustic stimuli - run experiments and collect data - process data and perform statistical analysis Finally, results will be presented at conferences and published in international journals.
WORK ENVIRONNEMENT The postdoctoral fellow will work at the Laboratoire Parole et Langage (http://www.lpl-aix.fr/), a laboratory whose research interests are extremely varied (including linguistics, phonetics, neuroscience, psycholinguistics, sociolinguistics, and computer science). He or she will benefit from this stimulating environment and interact with all the members of the laboratory (faculty members, other post-docs, engineers, doctoral students, etc.). He or she will have the opportunity to discover all the projects of the laboratory. EXPECTED PROFILE The postdoctoral fellow will have a PhD in the speech sciences and/or in psychoacoustics (auditory measurements, audio signal processing). A strong background in data processing and statistics is also be required. A good command of French and English will also be appreciated.
18 months. Beginning in autumn 2019 Monthly salary: ~?1900 net (depending on experience) Location : Laboratoire Parole et Langage (http://www.lpl-aix.fr/), Aix Marseille Université, CNRS UMR 7309, Aix-en-Provence, France For additional information: christine.meunier@univ-amu.fr
(2019-06-21) Ingénieur d'études, LIG, Univ.de Grenoble-Alpes, France
RECRUTEMENT D?UN INGÉNIEUR D?ÉTUDES EN TRAITEMENT AUTOMATIQUE DES LANGUES NATURELLES ET EN DÉVELOPPEMENT D?UNE INTERFACE IHM-WEB Début de contrat: Octobre 2019 Durée: 7 mois Salaire: 2000? brut/mois
Profil : - Titulaire d?un Master ou d?un doctorat en TAL - Une formation en sciences du langage sera appréciée. - Compétences opérationnelles en génie logiciel (gestion de version, tests, qualité de code) et Python
- Des compétences en C/C++ seraient un plus
- Une expérience en traitement automatique de la parole est requise ainsi qu?un bon niveau de français.
- Une experience en METEOR, firepad, Node.JS, mongoDB, firebase serait un plus.
Ce poste nécessite des capacités de travail en équipe et en autonomie. Une connaissance du contexte linguistique de la surdité serait un atout supplémentaire. *Description du projet et des missions* Dans le cadre du projet MANES (Médiation et Accessibilité Numérique pour les Étudiants Sourds) dirigé par François Portet (LIG), Isabelle Estève (LIDILEM) et Marion Fabre (ECP) dont une partie du financement est assurée par PULSALYS, IDEX Lyon-Saint Etienne, nous recrutons un ingénieur d?études pour un CDD de 7 mois. L?objectif général du projet est de développer un dispositif de sous-titrage en temps-réel pour rendre accessible le discours oral de l?enseignant aux étudiants sourds, de façon à favoriser l?appropriation individuelle des savoirs, par le biais de la prise de notes. La réalisation technologique et les capacités de traitement de l?écrit par les publics sourds seront les deux axes de ce projet.
La mission de l?ingénieur d?études consiste d?une part à développer, évaluer et améliorer des prototypes basés sur les dernières avancées scientifiques et à les fusionner pour réaliser un prototype de sous-titrage automatique en temps réel, à partir de la plateforme Kaldi. D?autre part, à concevoir une interface IHM pour la retranscription automatique en temps réel du discours de l?enseignant et la projection du sous-titrage en cours, en y intégrant le prototype mentionné ci-dessus. Le candidat aura en charge l?élaboration du prototype pour des expérimentations en salle de cours et les réajustements de l?interface liés à ces expérimentations.
Missions en Traitement automatique des Langues : - Prise de connaissance de l?état de l'art des systèmes de sous-titrage automatique - Test de transcription semi-automatique et vérification des extraits oraux de cours magistraux - Adaptation du système temps-réel de transcription automatique à réseaux de neurones (Kaldi) - Traitement en temps-réel des transcriptions pour le sous-titrage adapté aux publics sourds Les exigences fonctionnelles envisagées pour l?implémentation de la plateforme Kaldi sont : le repérage des mots-clés et des synonymes en temps réel (déjà existant dans KALDI) et le développement de nouvelles fonctionnalités : la segmentation et la simplification. Des perspectives d?adaptations pour le Off-Line seront aussi à envisager.
Missions en développement : - Réalisation d?une application web permettant la retranscription en temps réel du discours de l?enseignant et la projection de la retranscription obtenue (vidéo projecteur et possibilité d?extension pour une interface mobile) - Elaboration de l?interface étudiant : stockage, récupération de la trace écrite pour retravail et modification. - Elaboration de l?interface enseignant : paramétrisation des éléments clés du cours - Documentation : description et mode d?emploi de l?interface d?IHM.
*Environnement de travail* Le projet est porté par le laboratoire Education, Cultures et Politiques (ECP, EA 4571), Université Lumière Lyon 2 encollaboration avec le Laboratoire de Linguistique et Didactique des Langues Étrangères et Maternelles (LIDILEM), Université Grenoble-Alpes et le Laboratoire d'Informatique de Grenoble (LIG). Le poste sera accueilli physiquement au Laboratoire d'Informatique de Grenoble, UMR CNRS, au sein de l'équipe GETALP. L'équipe GETALP (https://lig-getalp.imag.fr) regroupe plus de 40 chercheurs, ingénieurs et étudiants dans le domaine du traitement automatisé des langues et de la parole multilingue.
(2019-06-21) Post doc at LIUM, Univ. du Mans, Le Mans, France
Post-doc position open ------------------------------------ The Speech and Language Technology Group in Le Mans University is looking for a post-doc scientist to develop autonomous systems
Keywords: Deep Learning, lifelong, autonomous systems, unsupervised learning, active-learning, interactive-learning
Context ------------------------------------ The LST team from LIUM (Le Mans University) is focusing on autonomous system?s behavior for the task of speaker diarization and machine translation. The ALLIES project (European Chist-ERA collaborative project) aims at developing evaluation protocols, metrics and scenarios for lifelong learning autonomous systems. The goal is to enable auto-adaptable systems that can also auto-evaluate in order to sustain their performance across time. Autonomous systems can rely on human domain experts via active and interactive learning processes to be define within the ALLIES project.
Missions ------------------------------------ Develop an autonomous system for speaker diarization by integrating lifelong learning, active and interactive learning components. The research work will be related to some of the following topics: - unsupervised adaptation - unsupervised evaluation - active learning (based on the unsupervised evaluation process, the autonomous system is free to require additional knowledge from the human domain expert) - Interactive learning (a human domain expert provides specific knowledge to the autonomous system. This information must be taken into account by the system) - Performance will be analyzed using protocols, metrics and scenarios developed for the ALLIES project.
Participation to the ALLIES benchmarking evaluation for speaker diarization. During the ALLIES project, LIUM is organizing two international evaluation campaigns (one for Speaker Diarization jointly organized with Albayzin and the second one for Machine Translation jointly with WMT) The benchmarking evaluation will serve to validate approaches developed during the post-doc
Dissemination The research will be published in the major conferences and journals
Supervisers: Anthony Larcher (anthony.larcher@univ-lemans.fr) and Loïc Barrault (loic.barrault@univ-lemans.fr)
Expected competences: - Phd in Machine Learning and Deep Learning - Experience in speech processing is positive - Python fluent - familiar with a deep learning toolkit (Pytorch, TensorFlow)
(2019-06-22) Responsable de IA H/F. Manager de l’équipe R&D, Zaion, Paris, France
ZAION est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l’Intelligence Artificielle.
ZAION a développé une solution qui s’appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 18 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP …).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c’est prendre part à une aventure passionnante au sein d’une équipe ambitieuse afin de devenir la référence sur le marché des robots conversationnels.
Dans le cadre de son développement ZAION recherche son Responsable de IA H/F. Manager de l’équipe R&D, votre rôle est stratégique dans le développement et l’expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu’ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse…) et donc adapter leurs réponses en conséquence.
Vos missions principales :
- Vous participez à la création du pôle R&D de ZAION et piloterez à votre arrivée votre premier projet de reconnaissance d’émotion dans la voix.
- Construisez, adaptez et faites évoluer nos services de détection d’émotion dans la voix
- Analysez de bases de données conséquentes de conversations pour en extraire les conversations émotionnellement pertinentes
- Construisez une base de données de conversations labelisées avec des étiquettes émotionnelles
- Formez et évaluez des modèles d'apprentissage automatique pour la classification d’émotion
- Déployez vos modèles en production
- Améliorez en continue le système de détection des émotions dans la voix
Qualifications requises et expérience antérieure :
-Vous avez une expérience de 5 ans minimum comme Data Scientist/Machine Learning appliqué à l’Audio et une appétence à l’encadrement
- Diplômé d’une école d’Ingénieur ou Master en informatique ou un doctorat en informatique mathématiques avec des compétences solides en traitements de signal (audio de préférence)
- Solide formation théorique en apprentissage machine et dans les domaines mathématiques pertinents (clustering, classification, factorisation matricielle, inférence bayésienne, deep learning...)
- La mise à disposition de modèles d'apprentissage machine dans un environnement de production serait un plus
- Vous maîtrisez un ou plusieurs des langages suivants : Python, Frameworks de machine Learning/Deep Learning (Pytorch, TensorFlow,Sci-kit learn, Keras) et Javascript
- Vous maîtrisez les techniques du traitement du signal audio
- Une expérience confirmée dans la labélisation de grande BDD (audio de préférence) est indispensable ;
- Votre personnalité : Leader, autonome, passionné par votre métier, vous savez animer une équipe en mode projet
- Vous parlez anglais couramment
Merci d’envoyer votre candidature à : alegentil@zaion.ai
“As an intern at Speechmatics I have worked on projects that use real machine learning to deliver real value to people across the world. There are few places where the machine learning being used is at the bleeding edge of the field, but Speechmatics is one of them. The company has an amazing culture that allows you to grow as a programmer and as a person. If you want to be a part of a fast-growing machine learning company where you, personally, will make a difference then Speechmatics could well be the place for you!”
Sam Ringer, Machine Learning Engineer (previously R&D Intern), Speechmatics
Background
Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.
At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.
The Opportunity
The Speechmatics Engineering team develops and maintains speech-oriented products and services that will be used by businesses worldwide and is responsible for the complete product development cycle for these products. In this internship, you’ll help to support fundamental speech and language processing research to improve our performance and language coverage as well as helping to build products and features to delight our users.
Because you will be joining a rapidly expanding team, you will need to be a team player who thrives in a fast-paced environment, with a focus on investigating ideas and rapidly moving research developments into products. We strongly encourage versatility and knowledge transfer within and across teams. You will be expected to learn fast and feel emboldened to ask for support as you need it.
Prior experience of speech recognition is desirable, although Speechmatics has a team of speech recognition engineers who will collaborate and share any specialised knowledge required. If you are enthusiastic about speech recognition and machine learning in general, with the drive to deliver the best possible technology solutions, then we want to hear from you!
Our internships are not time constrained to specific dates – we can work out mutually agreeable start and end dates as part of the application process.
Key Responsibilities
Exploring and evaluating research ideas
Increasing and improving our language coverage
Prototyping new and improved features
Helping the company to take your R&D through to production
Communicating your work internally
Requirements
Essential
Team player
Enthusiasm for speech recognition and machine learning
Technical understanding of speech recognition or related discipline
Ability to rapidly deliver on ideas
Competent in Python and/or C/C++
Have or be studying towards a degree involving speech recognition, machine learning / computer science or related field
Desirable
Practical experience of ASR and ML packages such as Kaldi, HTK or TensorFlow
Commercial experience of speech recognition
Software development experience
Salary
Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.
'As a Speech Recognition Engineer at Speechmatics, I work on solving a multitude of problems related to improving the accuracy and delivering new features for a global automatic speech recognition engine. As a member of the speech team, I work across every aspect of speech and implement the latest research in acoustic and language modelling. The team is supportive and also rich in terms of skills and backgrounds. Speechmatics offer progressive and rewarding opportunities in one of the best speech technology companies in the world.'
André Mansikkaniemi, Speech Recognition Engineer at Speechmatics
Background
Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.
At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.
The Opportunity
We are looking for a talented speech engineer to help us build the best speech technology for anybody, anywhere, in any language. You will be part of a team that is working on our core ASR capabilities to improve our speed and accuracy and develop novel features that we can support in all languages. Your work will feed into our ground-breaking framework to support the building of ASR models in every language pack published by the company. You will be responsible for keeping our system the most accurate and useful commercial speech recognition system available.
As you will be joining a small team, you will need to be a team player who thrives in a fast-paced environment, with a focus on rapidly moving research developments into products. Bringing skills into the team is as important as a can-do attitude. We strongly encourage versatility and knowledge transfer within the team, so that we can share efficiently what needs to be done to meet our commitments to the rest of the company.
Key Responsibilities
Research and development of improved speed and accuracy across our range of world leading ASR products and related features
Delivering the software that provides an easy-to-use feature rich ASR product for our customers
Enhancing our machine learning framework that robustly builds any language with the best possible performance
Taking data all the way from its raw form through to a finished model
Working within a team in an agile environment
Working closely with other technical teams and product team to deliver on the company’s technical vision
Requirements
Essential
Graduate degree in Statistics, Engineering, Mathematics, Computer Science
Knowledge of key natural language processing or related technologies, such as speech recognition, text-to-speech or natural language understanding
Experience working with standard speech and/or ML toolkits, e.g. Kaldi, KenLM, TensorFlow, etc.
Solid Python programming skills
Experience using Unix/Linux
Quick and enthusiastic learner
Excellent teamwork and communications skills
Analytical mind-set with a data-driven approach to making decisions and attention to detail
Desirable
Postgraduate degree in related discipline
Commercial work experience in ASR or a related field
Experience of working in an Agile framework
Expertise in modern speech recognition, including WFSTs, lattice processing, neural net (RNN / DNN / LSTM), acoustic and language models, decoding
Comprehensive knowledge of machine learning and statistical modelling
Experience in deep machine learning and related toolkits, e.g. Theano, Torch, etc.
Deep expertise in Python and/or C++ software development
Experience working effectively with software engineering teams or as a Software Engineer
Salary
Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.
'As a Speech Recognition Engineer at Speechmatics, I work on solving a multitude of problems related to improving the accuracy and delivering new features for a global Automatic Speech Recognition engine. As a member of the speech team, I work across every aspect of speech and implement the latest research in acoustic and language modelling. The team is supportive and also rich in terms of skills and backgrounds. Speechmatics offer progressive and rewarding opportunities in one of the best speech technology companies in the world.'
André Mansikkaniemi, Speech Recognition Engineer, Speechmatics
Background
Speech technology is one of the most popular discussion items at the moment, yet speech interaction is limited to “Alexa, turn on the light”, or “Siri, where is the nearest coffee shop?” We are taking speech technology to the next level using our expertise in machine learning and speech-to-text technology to enable our customers to use conversational speech recognition. Our solutions power subtitling on TV, content discovery for videos, compliance solutions in banks, improve efficiency of meetings, and many other use-cases. Our mission is to improve human communication with a global speech engine, that works and put speech back at the heart of communication.
At Speechmatics you’ll be working with some of the smartest minds in the industry, working on cutting-edge projects and deploying the latest machine learning techniques to disrupt the market, providing customers with the best speech technology available, all whilst immersed in a progressive and great company culture. You can enjoy benefits including, share options, healthcare, life assurance, Bike Doctor, massages, regular BBQs, Brew Dogs in the fridge, no red tape, a top end laptop and much more. We’re building a company that truly strives to be world-leading and we’re looking for people who wholeheartedly believe they can be additive to our culture, bring new ideas to the table and get stuff done. If that’s you, carry on reading.
The Opportunity
We are looking for a talented speech engineer to help us build the best speech technology for anybody, anywhere, in any language. You will be part of a team that is working on our core ASR capabilities to improve our speed and accuracy and develop novel features that we can support in all languages. Your work will feed into our ground-breaking framework to support the building of ASR models in every language pack published by the company. You will be responsible for keeping our system the most accurate and useful commercial speech recognition system available.
As you will be joining a small team, you will need to be a team player who thrives in a fast-paced environment, with a focus on rapidly moving research developments into products. Bringing skills into the team is as important as a can-do attitude. We strongly encourage versatility and knowledge transfer within the team, so that we can share efficiently what needs to be done to meet our commitments to the rest of the company.
Key Responsibilities
Research and development of improved speed and accuracy across our range of world leading ASR products and related features
Delivering the software that provides an easy-to-use feature rich ASR product for our customers
Enhancing our machine learning framework that robustly builds any language with the best possible performance
Taking data all the way from its raw form through to a finished model
Working within a team in an agile environment
Working closely with other technical teams and product team to deliver on the company’s technical vision
Requirements
Essential
Commercial experience in ASR or a related field
Graduate degree in Statistics, Engineering, Mathematics, or Computer Science
Expertise in modern speech recognition, including WFSTs, lattice processing, neural net (RNN / DNN / LSTM), acoustic and language models, decoding
Experience working with standard speech and/or ML toolkits, e.g. Kaldi, KenLM, TensorFlow, etc.
Solid Python programming skills
Experience using Unix/Linux
Drive to help those around you learn and improve every day
Excellent teamwork and communications skills
Analytical mind-set with a data-driven approach to making decisions and attention to detail
Desirable
Postgraduate degree in related discipline
Experience of working in an Agile framework
Comprehensive knowledge of machine learning and statistical modelling
Experience in deep machine learning and related toolkits, e.g. Theano, Torch, etc.
Deep expertise in Python and/or C++ software development
Experience working effectively with software engineering teams or as a Software Engineer
Salary
Competitive salary (dependent on experience), flexible working and some awesome benefits & perks.
Live for the wow | Build authentic relationships | Be the adventure
Innovation is what we do. We build, we iterate, we develop the next thing that delivers that wow moment. We see value in building long-term, authentic relationships that last and are based on trust and honesty. With our customers, our colleagues, our leaders, our suppliers or within our local community. Our journey should be fun and exciting. We will celebrate our successes and learn from our mistakes together along the way. We embrace learning and change to grow naturally and organically as a company and individuals. We trust, we’re honest, kind and respectful.
(2019-07-17) Chief Technical Officer (CTO) at ELDA
Chief Technical Officer (CTO)
Under the supervision of the CEO, the responsibilities of the Chief Technical Officer (CTO) include planning and supervising technical development of tools, software components or applications for language resource production and management. He/she will be in charge of managing the current language resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded projects.
This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.
Profile: ? PhD in Computer Science, Natural Language Processing, or equivalent ? Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.) ? Familiarity with open source and free software ? Knowledge of a statically typed functional programming language (OCaml preferred) is a plus ? Good level in English, with strong writing and documentation skills in English ? Dynamic and communicative, flexible to work on different tasks in parallel ? Ability to work independently and as part of a multidisciplinary team ? Citizenship (or residency papers) of a European Union country ? Good level in Python, knowledge of Django would be a plus ? Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)
Salary: Commensurate with qualifications and experience (between 45-55K?). Other benefits: complementary health insurance and meal vouchers
Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org
ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.
For further information about ELDA/ELRA, visit: ww.elra.info
(2019-07-19) Two Post-doctoral positions at Le Mans University , France
2 Post-doctoral positions at Le Mans University on Deep learning approaches speech processing
*Place of work* Le Mans University, Le Mans ? France
*Starting date* From now to June 2020
*Salary* between 2 300 and 2 600 ? /month
*Duration* 12 months and 24 months (can be combined in a 36 months position)
**************************************** 1st position ****************************************
* Context * The LST team from LIUM (Le Mans University) is focusing on autonomous system?s behavior for the task of speaker diarization and machine translation. The ALLIES project (European Chist-ERA collaborative project) aims at developing evaluation protocols, metrics and scenarios for lifelong learning autonomous systems. The goal is to enable auto-adaptable systems that can also auto-evaluate in order to sustain their performance across time. Autonomous systems can rely on human domain experts via active and interactive learning processes to be define within the ALLIES project.
* Missions * Develop an autonomous system for speaker diarization by integrating lifelong learning, active and interactive learning components. The research work will be related to some of the following topics: - unsupervised adaptation - unsupervised evaluation - active learning (based on the unsupervised evaluation process, the autonomous system is free to require additional knowledge from the human domain expert) - Interactive learning (a human domain expert provides specific knowledge to the autonomous system. This information must be taken into account by the system) Performance will be analyzed using protocols, metrics and scenarios developed for the ALLIES project.
Participation to the ALLIES benchmarking evaluation for speaker diarization. During the ALLIES project, LIUM is organizing two international evaluation campaigns (one for Speaker Diarization jointly organized with Albayzin and the second one for Machine Translation jointly with WMT) The benchmarking evaluation will serve to validate approaches developed during the post-doc
* Dissemination* The research will be published in the major conferences and journals
Expected competences: - Phd in Machine Learning and Deep Learning - Experience in speech processing is positive - Python fluent - familiar with a deep learning toolkit (Pytorch, TensorFlow)
**************************************** 2nd position ****************************************
* Context * The LST team from LIUM (Le Mans University) is focusing on evolutive end-to-end neural networks for speaker recognition. The Extensor project (French ANR funded) aims at developing novel architectures for end-to-end speaker recognition as well as explaining the behavior of those networks. The focus of Extensor is threefold: get rid of the legacy of bayesian system?s architecture and explore wider opportunities offered in deep learning; explore real end-to-end architectures exploiting the tax signal instead of classical features (such as MFCC of filterbanks); Develop tools for explainability in speaker recognition.
* Missions * Develop end-to-end speaker recognition system based on state-of-the-art approaches (x-vectors, sincnet?) Develop evolutive architectures making use of existing genetic algorithms and study their behavior. Participate to the three hackathons organized by the Extensor project in order to develop tools for evolutive neural network architecture and explainability for speaker recognition. Dissemination: the research will be published in the major conferences and journals
Expected competences: - Phd in Machine Learning and Deep Learning - Experience in speech processing is positive - Python fluent - familiar with a deep learning toolkit (Pytorch, TensorFlow)
--
Anthony Larcher Maître de Conférences, HDR / Associate Professor Directeur de l'Institut Informatique Claude Chappe co-responsable de la Spécialité Informatique Responsable de l'option Interface Personnes Systèmes Tél. +33 (0)2 43 83 38 30 Avenue Olivier Messiaen, 72085 - LE MANS Cedex 09 univ-lemans.fr
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Keywords: hate speech, social media, natural language processing.
The rapid development of the Internet and social networks has brought great benefits to women and men in their daily lives. Unfortunately, the dark side of these benefits has led to an increase in hate speech and terrorism as the most common and powerful threats on a global scale. Hate speech is a type of offensive communication mechanism that expresses an ideology of hatred often using stereotypes. Hate speech can target different societal characteristics such as gender, religion, race, disability, etc. Hate speech is the subject of different national and international legal frameworks. Hate speech is a type of terrorism and often follows a terrorist incident or event.
Social networks are incredibly popular today. Nowadays, Twitter, LinkedIn, Facebook and YouTube are used as a standard tool for communicating ideas, beliefs and feelings. Only a small percentage of people use part of the network for unhealthy activities such as hate speech and terrorism. But the impact of this low percentage of users is extremely damaging. For years, social media companies such as Twitter, Facebook and YouTube have invested hundreds of millions of dollars each year in the task of detecting, classifying and moderating hate. But these efforts are mainly based on manually revising the content to identify and remove offensive content, which is extremely expensive.
This thesis aims at designing automatic and evolving methods for the classification of hate speech in the field of social media. Despite the studies already published on this subject, the results show that the task remains very difficult. We will use semantic content analysis methodologies from automatic language processing (NLP) and methodologies based on deep learning (DNN) which is the revolution in the field of artificial intelligence. During this thesis, we will develop a research protocol to classify hate speech in the text in terms of hateful, aggressive, insulting, ironic, neutral, etc. character. This type of problem is placed in the context of the multi-label classification.
In addition, the problem of obfuscation of words in hate messages will need to be addressed. People who want to write hate speech on the Internet know that they risk being censored by rudimentary automatic systems of moderation. So, users try to obscure their words by changing the spelling or the spelling of words.
Among the crucial points of this thesis are the choice of the DNN architecture and the relevant representation of the data, ie the text of the internet message. The system designed will be validated on real flows of social networks.
Skills
Strong background in mathematics, machine learning (DNN), statistics
Following profiles are welcome, either: Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.
References :
T Gröndahl, L Pajola, M Juuti, M Conti, N Asokan (2018) ?All You Need is? Love?: Evading Hate-speech Detection, arXiv preprint arXiv:1808.09115
Wiegand, M., Klakow, D. (2008). Optimizing Language Models for Polarity Classification. In Proceedings of ECIR, pp. 612-616.
Wiegand, M., Ruppenhofer, J. (2015). Opinion Holder and Target Extraction based on the Induction of Verbal Categories. In Proceedings of CoNLL, pp. 215-225.
Wiegand, M., Ruppenhofer J., Schmidt A., C. Greenberg (2018) Inducing a Lexicon of Abusive Words ? A Feature-Based Approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Wiegand, M., Wolf, M., Ruppenhofer, J. (2017) Negation Modeling for German Polarity Classification. In Proceedings of GSCL.
Zhang Z., Luo L. (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a PhD student for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.
PhD position offered
We offer a four year PhD position funded by a scholarship with a yearly bench fee. The PhD work will consist of building an agent-based simulation in which we can investigate emergence of behavior in a cognitively realistic setting. This means that the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. The modeling will primarily focus on emergence of speech, but the simulation should be general enough that it can be easily adapted to other areas, such as traffic or economic interactions.
What we are looking for
We are looking for an enthusiastic student with a degree in artificial intelligence, cognitive science, linguistics or equivalent and who has experience programming agent-based or cognitive models, preferably in Python or C++. Knowledge of speech and speech processing is a bonus. The starting date is negotiable, but preferably no later than September 2019.
How to apply
Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.
(2019-07-29) Visiting postdoc at Vrije Universiteit, Brussels, Belgium
Visiting postdoc in
Cognitively Plausible Emergent Behavior
In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a short term (three-six months) visiting postdoc for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.
Position offered
We offer a three-six months visiting postdoc position funded by a scholarship and with a bench fee. The work should consist of agent-based simulation, or of experiments to investigate emergence of behavior in a cognitively realistic setting. This means that in a computer simulation, the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. Experiments should focus on factors that are typical for human settings, but that are generally idealized away, such as altruism, conflicts of interests and other 'non-rational' behaviors. We are most interested in modeling emergence of speech, but we welcome applications proposing other areas, such as traffic or economic interactions.
What we are looking for
We are looking for an enthusiastic postdoc with a track record in artificial intelligence, cognitive science, linguistics or equivalent and who has either experience programming agent-based or cognitive models, or who has experience with the interaction between computer models and experiments. The starting date is negotiable, but preferably no later than September 2019.
How to apply
Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. Be sure to include a short (1-page) outline of your proposed project in the letter of motivation, as well as a short planning. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.
(2019-08-02) Research engineer of Post-doc, at Eurecom, Inria, LIA, France
EURECOM (Nice, France), Inria (Nancy, France) and LIA (Avignon, France) are opening a 18-month Research Engineer or Postdoc position on speaker de-identification and voice privacy.
(2019-08-02) Ph.D. position in Softbank robotics and Telecom-Paris, France
Ph.D. position in Softbank robotics and Telecom-Paris Subject: Automatic multimodal recognition of users? social behaviors in human-robot interactions (HRI)
*Places of work* Softbank Robotics [SB] (Paris 15e) & Telecom Paris [TP] Palaiseau (Paris outskirt)
*Context* The research activity of the Ph.D. candidate will contribute to : - Softbank Robotics robot?s software NAOqi, within the Expressivity team responsible for ensuring an expressive, natural and fun interaction with our robots. - the Social Computing topic [SocComp.] of the S2a team [SSA] at Telecom-ParisTech, in close collaboration with other researchers and Ph.D. students of the team.
* Candidate profile* As a minimum requirement, the successful candidate should have: ? A master in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, cognitive sciences, affective computing, reinforcement learning, natural language processing, speech processing ? Excellent programming skills (preferably in Python) ? Excellent command of English ? Very good communication skills, commitment, independent working style as well as initiative and team spirit
Given the multidisciplinary aspect of the subject, priority will be given to multidisciplinary profiles. Ph.D. applicant?s interest in social robotics is required.
*Keywords* Human-Machine Interaction, Social Robotics, Deep Learning, Social Computing, Natural Language Processing, Speech Processing, Computer Vision, Multimodality
*How to apply* Applications should be sent as soon as possible (the first review of applications will be made in early September). The application should be formatted as **a single pdf file** and should include: ? A complete and detailed curriculum vitae ? A letter of motivation ? The academic credentials and the transcript of grades ? The contact of two referees
*Description* Social robotics, and more broadly human-agent interaction is a field of human-machine interaction for which the integration of social behaviors is expected to have great potential. 'Socio-emotional behaviors' (emotions, social stances) include thus the role and the reactions of the user towards the robot during an interaction. These behaviors could be expressed differently depending: -on the user (age, emotional state, ...): some users may have a dominant behavior with the robot, considering it a tool to achieve a goal. Others are more cooperative with the robot, they can be more friendly with it. Still others try to trap or 'troll' the robot. -on the interaction context (users do not behave in the same way when interacting with a pepper selling toys, or with a pepper bank secretary). Besides, in each of these situations, the robot must be able to adapt its behavior, and to provide a coherent interaction between the user and the robot, avoiding confusion and frustration.
This Ph.D. will focus on multimodal modeling for the prediction of the user's socio-emotional behaviors during interactions with a robot and on building an engine that is robust to real-life scenarios and different contexts. In particular, the Ph.D. candidate will address the following points: - the encoding of contextual multimodal representations relevant for the modeling of socio-emotional behavior. Thanks to the robot, we have access to a lot of information on context (market, robot intention, demographics, multi or mono user interaction, etc.) that could be combined to our multimodal representation. - the development and evaluation of models that take advantage of the complementarity of modalities in order to monitor the evolution of the user's socio-emotional behaviors during the interaction (e. g. taking into account the inherent sequentially of the interaction structure) The models will be based on sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou] and [BenYoussef19].
Selected references of the team: [Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews. in AAAI 2019 [Garcia] Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d?Alche-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, ICML 2018 [Clavel&Callejas] Clavel, C.; Callejas, Z., Sentiment analysis: from opinion mining to human-agent interaction, Affective Computing, IEEE Transactions on, 7.1 (2016) 74-93. [Langlet] C. Langlet and C. Clavel, Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user?s likes and dislikes , in ACL 2015 [Maslowski] Irina Maslowski, Delphine Lagarde, and Chloé Clavel. In-the-wild chatbot corpus: from opinion analysis to interaction problem detection, ICNLSSP 2017. [Ben-Youssef17] Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim. Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464?472. ACM, 2017. [Ben-Youssef19] Atef Ben Youssef; Chloe Clavel; Slim Essid Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction, IEEE Transactions on Affective Computing, 2019 [Varni] Varni G., Hupont, I., Clavel, C., Chetouani, M. Computational Study of Primitive Emotional Contagion in Dyadic Interactions. IEEE Transactions on Affective Computing, 2017.
(2019-08-12) Several positions in Forensic Speech Science or Forensic Data Science: Aston University, Birmingham, UK
Positions in Forensic Speech Science or Forensic Data Science:
- One Lecturer or Senior Lecturer
- Two Postdoctoral Researchers
Aston University, Birmingham, UK
Aston University has recently been awarded GBP 5.4 M from Research England?s Expanding Excellence in England (E3) Fund. The money is being used to expand the existing Centre for Forensic Linguistics into the substantially larger Aston Institute for Forensic Linguistics (AIFL). As part of the expansion, we are building a research team with expertise in forensic speech science and in forensic data science. In addition to conducting research in forensic speech science, members of the team will work on forensic inference and statistics more broadly, and on quantitative-measurement and statistical-model based approaches in other branches of forensic science. The latter potentially include but are not limited to: fingerprints, face, gait, ballistics, blood pattern analysis, and linguistics. The Forensic Speech Science Laboratory and the Centre for Forensic Data Science will be headed by Dr Geoffrey Stewart Morrison, and, in addition to the affiliation with AIFL, will be affiliated with the Computer Science Department in the School of Engineering and Applied Science.
We are looking to recruit the following positions:
Lecturer or Senior Lecturer in Forensic Speech Science or Forensic Data Science
Closing Date: 23.59 hours BST on September 30, 2019
Interview Date: To be confirmed
The Lecturer or Senior Lecturer position will be a full-time permanent position and will include teaching and administrative responsibilities. The position is costed as a Grade 9 Lecturer, but an exceptionally well qualified and experienced successful applicant could potentially be appointed as a Grade 10 Senior Lecturer. Note: ?Lecturer? is equivalent to North American ?Assistant Professor?, ?Senior Lecturer? is equivalent to North American ?Associate Professor?, and ?Reader / Associate Professor? is an occasionally used additional rank between Senior Lecturer and Professor.
The Postdoctoral Researcher positions may be filled as full-time appointments (preferred) or via a combination of part-time appointments. The Postdoctoral Researcher positions will be fixed-term, but the plan is to build a team that will be successful in obtaining additional research funding that will sustain these positions.
All new team members must have a commitment to solving forensic problems. Previous experience working on forensic problems would be advantageous, but not essential. A background in forensic speech science, in other branches of forensic science, and/or in forensic inference and statistics would be advantageous, but not essential. At least one of the new team members must have a strong background in state-of-the-art automatic speaker recognition, with an ability to implement systems. Other useful backgrounds for members of the team would include biometrics, machine learning, natural language processing, and acoustic phonetics.
Candidates may apply for both the Lecturer / Senior Lecturer and the Research Associate positions. If positions are not filled after this round of recruitment, we will initiate another round of recruitment.
We also welcome enquiries from individuals who have obtained or are applying for their own postdoctoral fellowships, e.g., Marie Sklodowska-Curie Fellowships. For suitable candidates we would assist with the application process.
Potential candidates are encouraged to contact Dr Geoffrey Stewart Morrison to seek more information about these positions.
Tel: +44 121 204 3901
e-mail: g.s.morrison@aston.ac.uk
Dr Morrison will be attending Interspeech in September and would be happy to meet informally with potential applicants there.
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 ASAP.
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:
The application deadline is ** September 13, 2019 **
----------------- Iolanda Leite Assistant Professor KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division 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
(2019-08-17) Fully funded PhD position at IDIAP, Martigny, Valais, Switzerland.
There is a fully funded PhD position open at Idiap Research Institute on spiking neural architectures for speech prosody.
The research will build on work done recently at Idiap on creating tools for physiologically plausible modelling of speech. The current 'toolbox' contains rudimentary muscle models and means to drive these using conventional (deep) neural networks. The main focus of the work will involve use of spiking neural networks such as the 'integrate and fire' type that is broadly representative of those found in biological systems. Whilst we have focused so far on prosody (actually intonation), the application is open ended; the focus is on the neural modelling. A key problem to be solved will be that of training of the spiking networks, especially with the recurrence that is usual in such networks. We hope to be able to train and use spiking networks as easily as conventional backpropagation networks, and to shed light on current understanding of how biological spiking networks learn (e.g., via spike timing-dependent plasticity).
Idiap is located in Martigny in French speaking Switzerland, but functions in English and hosts many nationalities. PhD students are registered at EPFL. All positions offer quite generous salaries. Martigny has a distillery and a micro-brewery and is close to all manner of skiing, hiking and mountain life.
(2019-08-18) PhD positions at IRIT, Toulouse, France
Applications are invited for a three-year Early Stage Researcher PhD positions in the speech technology for pathological speech.
Description
The thesis focuses on studying the link between the internal representations of Deep Neural Networks (DNNs) and the subjective representation of speech intelligibility. We propose to explore the saliency detection capabilities of DNNs when used in a regression task for predicting speech intelligibility scores as given by human experts. By saliency, we mean to retrieve which frequency bands are important and used by a DNN to make its predictions.
The final expectation is to identify regions of interest in the speech signal, both in time and frequency, that characterise the level of speech impairment.
The experiments will be processed on various samples of speech performed by 150 people (100 patients and 50 healthy controls). This database was recorded within the INCA C2SI project, and contains speech from patients treated for cancer of the oral cavity or pharynx. It contains also various metadata such as the location of the tumor, the impairment in terms of severity and intelligibility that were appreciated by human experts, self evaluation questionnaires on the patient?s quality of life? Various tasks were recorded such as a sustained vowel, read speech, nonsense words, prosodic exercises, picture description, etc.
There will be also the possibility to extend the work to another corpus which is composed of voice of patients suffering from Parkinson disease.
At first, the PhD will have to take benefit from the various analysis and descriptions that were done during the C2SI project trying to correlate the impact of the tumor and the communication ability. Those results will help attesting the human representation of the impact of the disease. Then, a DNN representation will be modeled to fit the data, taking care of the data sparsity. The last part of the work will be to explore the intern representation of the DNN, trying to explore what part of the signal help to make a decision on the impact of the disease and that will be the final goal of the thesis, studying the automatic representation that lies in the model the student will propose.
This work is funded by the TAPAS project (https://www.tapas-etn-eu.org) which is a Horizon 2020 Marie Sk?odowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project that aims to transform the well being of people across Europe with debilitating speech pathologies (e.g., due to stroke, Parkinson's, etc.). These groups face communication problems that can lead to social exclusion. They are now being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but which is not robust to atypical speech.
The supervision of the PhD will take place at IRIT laboratory by the SAMoVA team in Toulouse. SAMoVA does research in the domain of ?analysis, modeling and structuring of audiovisual content?. The application areas are diverse: speech processing, identification of languages, speaker verification and speech and music indexing. The researchers expertise covers novel machine learning and audio processing technologies and is now focused on deep learning methods, leading to several publications in international conferences.
Eligibility Criteria:
Early Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers.
- The ESR may be a national of a Member State, of an Associated Country or of any Third Country. - The ESR must not have resided or carried out her/his main activity (work, studies, etc.) in the country of her/his host organization for more than 12 months in the 3 years immediately prior to her/his recruitment. - Holds a Master?s degree or equivalent, which formally entitles to embark on a Doctorate. - Does not hold a PhD degree.
Keywords: discriminative patternmining, neural networks analysis, explainability of black box models, speech recognition.
Deadline to apply: September 30th, 2019
Context:
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminativepatternmining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in patternmining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year) post-doc with the following preferred skills: ? Some knowledge (interest) about speech recognition ? Knowledgeable in patternmining (discriminative patternmining is a plus) ? Knowledgeable in machine learning in general and deep learning particular ? Good programming skills in Python (for Keras and/or Tensor Flow) ? Very good English (understanding and writing)
See the INRIA web site for the post-doc page.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in September for an expected start in October-November 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015.
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
(2019-08-28) Speech technologist/linguist at Cobaltspeech.
Cobalt Speech & Language (http://www.cobaltspeech.com/ ) is looking for a speech technologist/linguist to help find and create language resources for a project in French Canadian.
The project is short term (<2 months) part time (~5-8h a week) which is ideal for a student to get an experience with speech industry.
The following skills are required: - Native French (does not have to be Canadian - though desirable) - Able to communicate in English - Basic understanding of speech technology and linguistics - Ability to run a python script.
52019-09-05) Post doctoral position at IDIAP, Martigny, Switzerland
The Social Computing Group at Idiap is seeking a creative and motivated postdoctoral researcher to work on deep learning methods for behavioral analysis from video and audio data. This is an opening for a researcher with experience in deep learning applied to dynamic human behavior (from voice, body, or face), in the context of a project funded by Innosuisse, the Swiss funding agency for promotion of innovation.
The position offers the opportunity to do exciting work on deep learning and social behavior. The researcher will work with Prof. Daniel Gatica-Perez and his research group. The candidates will have a PhD degree in computer science or engineering, with proven experience in deep learning and a strong publication record.
Salaries are competitive and starting date is immediate. Interviews will start upon reception of applications until the positions are filled.
Interested candidates are invited to submit a cover letter, a detailed CV, and the names of three references through Idiap's online recruitment system:
Interested candidates can also contact Prof. Daniel Gatica-Perez (gatica@idiap.ch).
About Idiap Research Institute
Idiap is an independent, not-for-profit, research institute recognized and funded by the Swiss Federal Government, the State of Valais, and the City of Martigny. Idiap is an equal opportunity employer, and offers competitive salaries and excellent working conditions in a dynamic and multicultural environment.
Idiap is located in the town of Martigny in Valais, a scenic region in the south of Switzerland, surrounded by the highest mountains of Europe, and offering exceptional quality of life, exciting recreational activities, including hiking, climbing and skiing, as well as varied cultural activities, all within close proximity to Lausanne and Geneva. English is the official working language.
ZAION est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l’Intelligence Artificielle.
ZAION a développé une solution qui s’appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 18 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP …).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c’est prendre part à une aventure passionnante au sein d’une équipe ambitieuse afin de devenir la référence sur le marché des robots conversationnels.
Nous rejoindre, c’est prendre part à une aventure passionnante et innovante afin de devenir la référence sur le marché des robots conversationnels. Dans le cadre de son développement ZAION recrute son Data Scientist /Machine Learning appliqué à l’Audio H/F. Au sein de l’équipe R&D, votre rôle est stratégique dans le développement et l’expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu’ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse…) et donc adapter leurs réponses en conséquence.
Vos missions principales :
- Vous participez à la création du pôle R&D de ZAION et piloterez à votre arrivée votre premier projet de reconnaissance d’émotion dans la voix.
- Construisez, adaptez et faites évoluer nos services de détection d’émotion dans la voix
- Analysez de bases de données conséquentes de conversations pour en extraire les conversations émotionnellement pertinentes
- Construisez une base de données de conversations labelisées avec des étiquettes émotionnelles
- Formez et évaluez des modèles d'apprentissage automatique pour la classification d’émotion
- Déployez vos modèles en production
- Améliorez en continue le système de détection des émotions dans la voix
Qualifications requises et expérience antérieure :
-Vous avez une expérience de 2 ans minimum comme Data Scientist/Machine Learning appliqué à l’Audio
- Diplômé d’une école d’Ingénieur ou Master en informatique ou un doctorat en informatique mathématiques avec des compétences solides en traitements de signal (audio de préférence)
- Solide formation théorique en apprentissage machine et dans les domaines mathématiques pertinents (clustering, classification, factorisation matricielle, inférence bayésienne, deep learning...)
- La mise à disposition de modèles d'apprentissage machine dans un environnement de production serait un plus
- Vous maîtrisez un ou plusieurs des langages suivants : Python, Frameworks de machine Learning/Deep Learning (Pytorch, TensorFlow,Sci-kit learn, Keras) et Javascript
- Vous maîtrisez les techniques du traitement du signal audio
- Une expérience confirmée dans la labélisation de grande BDD (audio de préférence) est indispensable ;
- Votre personnalité : Leader, autonome, passionné par votre métier, vous savez animer une équipe en mode projet
- Vous parlez anglais couramment
Merci d’envoyer votre candidature à : alegentil@zaion.ai
(2019-09-15) Post-doc and research engineer at INSA, Rouen, Normandy, France
Post-doctoral position (1 year): Perception for interaction and social navigation
Research Engineer (1 year): Social Human-Robot Interactions
Laboratory: LITIS, INSA Rouen Normandy, France
Project: INCA (Natural Interactions with Artificial Companions)
Summary:
The emergence of interactive robots and connected objects has lead to the appearance of symbiotic systems made up of human users, virtual agents and robots in social interactions. However, two major scientific difficulties are unsolved yet: on the one hand, the recognition of human activity remains inaccurate, both at the operational level (location, mapping and identification of objects and users) and cognitive (recognition and tracking of users? intentions) and, on the other hand, interaction involves different modalities that must be adapted according to the context, the user and the situation. The INCA project aims at developing artificial companions (interactive robots and virtual agents) with a particular focus on social interactions. Our goal is to develop new models and algorithms for intelligent companions capable of (1) perceiving and representing an environment (real, virtual or mixed) consisting of objects, robots and users; (2) interacting with users in a natural way to assess their needs, preferences, and engagement; (3) learning models of user behavior and (4) generating semantically adequate and socially appropriate responses.
Post-doctoral position in perception for interaction and social navigation (1 position)
The candidate will work to ensure that a robot can recognize the physical content of the scene surrounding him, recognize himself, static and dynamic objects (users and other robots) and finally predict the movement of dynamic elements. The integration of data from different sensors should allow the mapping of an unknown environment and estimate the position of the robot. First, VSLAM techniques (Visual Simultaneous Localization And Mapping) (Saputra 2018) will be used to map the scene. The regions (or points) of interest detected could then be used to detect obstacles. In order to distinguish between static and dynamic objects, methods of separating the background from the foregound of the scene (Kajo et al, 2018) will be used. Finally, some recent techniques of the Flownet 2.0 type (Eddy et al, 2017), for the prediction of the motion on a video sequence should make it possible to predict the next movement of an object dynamic object and the to apprehend its behavior.
Profile: the candidate must have strong skills in mobile robotics and navigation techniques (VSLAM, OrbSlam, Optical Flow, stereovision...) and a high programming capacities under ROS or any other programming language compatible with robotics. Machine learning and Deep learning skills will be highly appreciated.
Research Engineer in Social Human-Robot Interactions (1 position)
The hired research engineer will work closely with the INCA research staff (permanent, PhD and post-doctoral members) and other project partners. This will mainly involve administering the project's Pepper robots, developing the necessary tools, integrating the algorithms developed with the AgentSlang platform (https://agentslang.github.io/) and join the team created to participate in the Robocup 2020 in Bordeaux, @Home league.
Profile: Computer Sciences / Robotics Engineer
Good level in programming (ROS, Python, possibly Java)
Strong knowledge in robotics
Experiences in some of the following areas would be a plus (non-exhaustive list): machine learning, human-machine social interactions, scene perception, spatio-temporal and semantic representation, natural language dialogue.
Duration and remuneration: 1 year, 2480euros/month (gross salary)
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminative pattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year)post-doc with the following preferred skills:
? Some knowledge (interest) about speech recognition
? Knowledgeable in pattern mining (discriminative pattern mining is a plus)
? Knowledgeable in machine learning in general and deep learning particular
? Good programming skills in Python (for Keras and/or Tensor Flow)
? Very good English (understanding and writing)
However, good PhD applications will also be considered and, in this case, the position will last 3 years. The position will be funded by INRIA (https://www.inria.fr/en/). See the INRIA web site for the post-doc and PhD wages.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in June for an expected start in September 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
[8] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
[9] G. Saon, H.-K. J. Kuo, S. Rennie, M. Picheny: The IBM 2015 English conversational telephone speech recognition system?, Proc. Interspeech, pp. 3140-3144, 2015.
[10] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke : The Microsoft 2017 Conversational Speech Recognition System, IEEE ICASSP, 2018.
10h15-11h15 Debra Ziegeler, conférencière invitée (U. Sorbonne Nouvelle) : The future of already in Singapore English: a matter of selective convergence
11h15-11h45 Pause-café
11h45-12h30 Diana Lewis (AMU, LPL) : Grammaticalisation de lexème, de construction : deux cas de développement adverbial en anglais
12h30-14h15 Déjeuner
14h15-15h00 James German (AMU, LPL) : Linguistic adaptation as an automatic response to socio-indexical cues
15h00-15h45 Daniel Véronique (AMU, LPL) : L’« agglutination nominale » dans les langues créoles françaises : un exemple de convergence ?
15h45-16h15 Pause-Café
16h15-17h00 Chady Shimeen-Khan (U. Paris Descartes, CEPED) : Convergences et divergences à des fins discursives à travers l’usage des marqueurs discursifs chez les jeunes Mauriciens plurilingues
17h00-17h45 Sibylle Kriegel (AMU, LPL) : Créolisation et convergence : l’expression du corps comme marque du réfléchi
17h45-18h15 Charles Zaremba : Le CLAIX Cercle Linguistique d’Aix-en-Provence, retrospective
Samedi 19 octobre
10h-10h45 Akissi Béatrice Boutin (ILA-UFHB, Abidjan-Cocody) : Réanalyses avec et sans convergence dans le plurilinguisme ivoirien
10h45-11h30 Massinissa Garaoun (AMU) : Convergence linguistique et cycles : le cas de la négation en arabe maghrébin et en berbère
11h30-12h Pause-café
12h-12h45Nicolas Tournadre (AMU, LACITO) : Phénomènes de copie et de convergence dans les langues du Tibet et de l’Himalaya
12h45-13h30Cyril Aslanov (AMU, LPL) : Convergence and secondary entropy in a macrodiachronic perspective
Le candidat retenu intégrera une équipe en forte croissance spécialisée en évaluation des systèmes d’IA, ainsi qu’un projet européen ambitieux portant sur les systèmes de traitement de la langue évolutifs (en traduction et en diarisation). La caractérisation des performances des systèmes intelligents capables de s’auto-améliorer au fur et à mesure de leur utilisation, par eux-mêmes et par interaction avec l’humain utilisateur, représente un véritable défi que ce post-doctorat propose de relever.
(2019-10-06) offre d'emploi ZAION/ANNOTATEURS,Paris France
ZAION (https://www.zaion.ai) est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.
ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 12 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une belle aventure au sein d?une équipe dynamique qui a l?ambition de devenir la référence sur le marché des robots conversationnels.
Au sein de notre activité Intelligence Artificielle, pour appuyer ses innovations constantes concernant l'identification automatique des sentiments et émotions au sein d'interactions conversationnelles téléphoniques, nous recrutons un Annotateur/Transcripteur H/F :
Ses missions principales :
ANNOTER avec exactitude les échanges entre un client et son conseiller selon des balises expliquées sur un guide,
travailler avec minutie à partir de documents audio et texte en français,
se familiariser rapidement avec un logiciel d'annotation dédié,
connaître les outils de travail collaboratif,
utiliser ses connaissances culturelles, langagières et grammaticales pour rendre compte avec une grande précision non seulement de la conversation entre deux interlocuteurs sur un sujet donné mais aussi de la segmentation de leurs propos.
Le profil du candidat :
être locuteur natif et avoir une orthographe irréprochable,
avoir une très bonne maîtrise des environnements Mac OU Windows OU Linux, - faire preuve de rigueur, d?écoute et de discrétion. Contrat en CDD (temps complet), basé à Paris (75017) Si intéressé(e), prière de contacter Anne le Gentil/RRH à l?adresse suivante : alegentil@zaion.ai en joignant au mail un C.V
