| 6-1 | (2016-09-04) postdoctoral position on the Evaluation of an Adaptive Training Virtual Environment, Heudiasyc Lab CNRS
We are inviting applications for one postdoctoral position on the Evaluation of an Adaptive Training Virtual Environment at Heudiasyc Lab CNRS (https://www.hds.utc.fr/).
Context
Feedback selection is a major issue for intelligent virtual environments for training. Expert knowledge provides useful insight, but it can be difficult to collect. Furthermore, the influence of a specific type of feedback may vary across trainees and over training, which is rarely reflected in expert knowledge. As part of an automatic gesture training system, we modelled the co-evolution between a trainee and a training environment. We consider trainees by their levels in every dimensions of the task, and the training environment as the relevance of every element in its set of feedback. Over time, this co-evolution is reflected by trainee successive performances, and changes in the feedback selection. We model these interactions as a multi-armed bandit problem, each arm representing a type of feedback. This allows to adapt feedback selection only relying on the interactions between the trainee and the training environment, without any prior knowledge. Combining the trainee and the set of feedback in a single representation space, we show how our model can provide useful indicators regarding trainees progression and feedback relevance.
Objective
The goal of this post-doc is to evaluate the impact of adaptation in training virtual environments. We believe that co-evolution based on the enaction theory has a positive impact on training. The candidate will design an experiment in order to validate the model. Two platforms are available to conduct this experiment: a 2D platform (PhD Remy Frenoy) and a 3D platform (extension of the model in a CAVE). One major issue is the role of feedback for controlling the interaction. A second issue is the impact of giving an importance to errors in training, in order to propose a flexible, personalized and easy of use training.
The position is for two years, starting before 15th December 2016. The successful candidate will join the Heudiasyc research team
(https://www.hds.utc.fr/).) at the CNRS premises within the Campus of the UTC (http://www.utc.fr/the_university/index.php), in Compiegne, France. The research activity will be carried out under the supervision of Indira Thouvenin (http://www.hds.utc.fr/~ithouven
Professor/ Enseignant-Chercheur HDR / http://www.hds.utc.fr/~ithouven
UMR CNRS 7253 Heudiasyc
Sorbonne Universités, Université de technologie de Compiègne
Génie Informatique - bureau 141
57 avenue de Landshut 60203 Compiègne (France)
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| 6-2 | (2016-10-03) Ph grant at the laboratoire de phonétique de l’Université de Mons, Belgium
Offre de bourse de doctorat
_____________________________________________________________________________________
Service de Métrologie et Sciences du Langage, Laboratoire de
phonétique,
Université de Mons, Mons, Belgique
_____________________________________________________________________________________
Le service de métrologie et sciences du langage (laboratoire de phonétique de l’Université de Mons,
Belgique) recherche, un spécialiste (M/F) de l’étude de la parole humaine désireux de préparer, en son
sein, une thèse de doctorat.
Profil du candidat (M/F) :
Secteur de formation initiale :
Sciences du langage (linguistique, logopédie, psychologie du langage,…) à titre de formation de
base ou à tout le moins de formation complémentaire approfondie.
Niveau à l’entrée :
• Au moins niveau « master » (« bac+5 », 300 crédits) au sens du décret de la Communauté
Française de Belgique organisant l’enseignement supérieur.
Compétences transversales
• Aptitude au travail en équipe, créativité, autonomie, curiosité scientifique.
• Bonne maîtrise de l’outil informatique (tableurs, gestion de bases de données, traitements de
texte).
• Maîtrise au moins quasi-native de la langue française à l’écrit et à l’oral.
• Maîtrise de la langue anglaise au moins au niveau C1 du CECR dans les 4 compétences .
Une sensibilité à la problématique des langues étrangères, la maîtrise d’autres langues, des connaissances
en phonétique (en particulier l’analyse objective de la parole), des connaissances et des compétences en
mathématiques appliquées (en particulier en statistiques), constituent des atouts complémentaires.
Track record
La personne recrutée peut se prévaloir de mentions égales ou supérieures à la grande distinction en
bachelier et en master.
Profil du poste:
La personne recrutée se voit octroyer une bourse de doctorat d’un an.
Elle s’engage à tenter d’obtenir, durant ce terme, un financement plus étendu en concourant pour des
postes tels que chercheur FRIA, FRESH et/ou aspirant FNRS.
Au terme de la première année, en cas d’absence du financement alternatif ainsi recherché, la personne
est évaluée par le comité d’accompagnement. Sur cette base, elle peut se voir octroyer –ou non- un
terme additionnel de 2 ans. Durant ce nouveau terme, elle s’engage à poursuivre sa recherche de
financements extérieurs de son doctorat.
En aucun cas, la durée cumulée des bourses de recherche doctorale ne peut excéder 4 années.
Prise de fonctions :
au plus tôt.
Les personnes intéressées sont priées d’adresser, pour le 30 octobre au plus tard, un dossier
comportant :
• une lettre de motivation ;
• un curriculum vitae ;
• tout document jugé utile ;
au format pdf (exclusivement) à l’adresse : bernard.harmegnies@umons.ac.be
Projet scientifique
Des concepts tels que fatigue ou stress sont fréquemment évoqués tant en sciences de la
vie qu’en sciences humaines. Ils se caractérisent non seulement par le fait que leur portée
s’étend aussi bien à la biochimie qu’au psychisme humains, mais surtout par l’idée qu’une
action sur l’esprit peut ici avoir des répercussions sur le corps et vice-versa. Ces notions
demeurent pourtant variablement définies et diversement objectivées, comme l’est aussi,
dans ce contexte, l’interaction entre physiologie et psychisme.
Le projet dans le cadre duquel s’inscrit le doctorat pour lequel il est ici fait appel vise à
élucider ces relations complexes en étudiant l'évolution conjointe, dans une approche intrasujets,
de trois types de variables: (i) des variables situationnelles (tant à variation invoquée
qu’à variation provoquée); (ii) des marqueurs biologiques de l'état du sujet humain
(approche métabonomique et bio-marqueurs spécifiques enregistrés au sein de divers
biofluides); (iii) les mesures révélatrices du traitement du langage par le sujet (gestion de la
parole en émission et en réception).
Les contextes dans lesquels sont recueillies les observations sont ceux du contrôle par le
sujet humain de processus complexes, spécialement en aéronautique, domaine hautement
générateur de « situations-problèmes » potentiellement suscitatrices de phénomènes.
Le doctorat se centre plus particulièrement sur les variables situationnelles et cible
spécifiquement celles qui sont liées aux langues utilisées par le sujet. On sait aujourd’hui que
la réalité physique des sons de parole est notamment influencée par divers facteurs qui s’y
rapportent. Ceux-ci peuvent être liés à l’inscription communautaire du sujet (on peut par
exemple citer les variabilités diatopique, diastratique, ou diachronique), voire aux actions
exogènes visant délibérément à modifier les caractéristiques phoniques des sons de parole,
par exemple dans les contextes de l’enseignement/apprentissage (et/ou de l’utilisation) de
langues non-maternelles. D’autres déterminants, de nature endogène au sujet, peuvent
également se manifester, qu’ils trouvent leur origine dans la sphère cognitive (maîtrise des
langues, expertise multilingue, etc.), soit dans la sphère affective (posture personnelle par
rapport aux langues utilisées). Ces travaux qui, à des titres divers, démontrent l’action de ces
facteurs endogènes sur les productions vocales, ouvrent la voie à un positionnement moins
descriptif mais plus centré sur la valeur indicielle des observations effectuées: puisque ces
facteurs ont un effet sur le signal vocal, la détection de leurs marques dans le signal ouvre la
voie d’une caractérisation, par la seule analyse des productions phoniques, de l’état du
locuteur.
Le secteur des transports, et en particulier celui de l’aéronautique, s’est montré
graduellement plus intéressé par ces perspectives lors des dernières décennies ; à un
moment où nombre d’incidents ou d’accidents y sont aujourd’hui imputables au facteur
humain plus qu’à des défauts techniques, le développement de recherches susceptibles de
contribuer à l’élaboration de systèmes d’alerte propres à détecter des altérations de la
fonctionnalité du pilote et ce, sur la base de variations du seul signal vocal, fait figure de défi
stratégique.
Si plusieurs recherches ont certes démontré l’intérêt de ces perspectives, force est
cependant de constater que les résultats en sont extrêmement diversifiés, voire parfois
contradictoires. Ceci s’explique probablement d’une part par l’insuffisance du volume de
données global recueilli et d’autre part par l’importante diversité méthodologique qui
caractérise le champ. De ce point de vue, trois dimensions apparaissent nécessiter une
attention particulière. D’une part, ces recherches, sont d’ordinaire restreintes à des
productions vocales anglophones, laissent dans l’ombre les autres langues de
communication aéronautique et, ipso facto, négligent les interactions possibles entre le
facteur langue et les divers facteurs étudiés ; peu, par ailleurs, prennent en considération le
caractère fréquemment multilingue des communications aéronautiques et le fait que,
souvent, les agents sont amenés à s’exprimer dans une langue non maternelle ; aucune, par
ailleurs, n’interroge le problème de la perte différentielle de compétence phonique en L2 et
en L1 sous l’effet des conditions adverses de communication.
La thèse visée se centrera en conséquence tant sur les effets exercés par les variables
situationnelles liées aux situations de contrôle de processus complexes sur la performance
multilingue que sur les effets de divers types de multilinguismes sur l’efficacité du contrôle
de processus complexes gérés en contextes multilingues.
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| 6-3 | (2016-10-06) Two positions at DefinedCrowd Corporation at its RandD center in Lisbon, Portugal
We’re DefinedCrowd Corporation (http://www.definedcrowd.com), headquartered in Seattle, Washington, with a R&D center in Lisbon, Portugal. We provide machine learning training data for artificial intelligence products and applications internationally. Our clients ranges from Fortune 500 companies to cutting-edge AI and robotics companies and we just got invested by Amazon and Sony. We are a Microsoft Accelerator alumni company, and we are recognized as the fastest growing company in the big data arena for AI. As we continue to expand our international footprint at DefinedCrowd, we are looking for talented new members to join this energetic, hardworking and fun team in both Seattle headquarter and in Lisbon R&D center. As we continue to grow at DefinedCrowd, we are constantly challenging ourselves with finding the most efficient way to manage business operation, building most suitable and sustainable models for our lines of business, creating a highly recognized and trusted brand among both clients and crowd communities.
Current job offers:
Data Scientist (Seattle or Lisbon)
We’re looking for a Data Scientist with an NLP background reporting to the CTO to drive the data science strategy and quality control using machine learning for DefinedCrowd.
Main requirements
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MSc/PhD in Computer Science or equivalent with experience in Machine Learning (including DNNs), Artificial Intelligence or Statistics.
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Creative thinking with the ability to drive their ideas into technology.
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NLP background or experience.
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Proactivity, initiative, positive attitude, ability to solve problems creatively.
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Excellent speaking and writing skills in English.
Nice to have
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Enterprise experience in this area.
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Experience with both supervised and unsupervised learning.
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Data warehousing and ETL.
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Knowledge of ML platforms such as Azure ML, Google Tensor, IBM Watson.
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Experience in statistical analysis and respective tools (e.g. SPSS).
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International publications in this area.
Technical Program Manager (Seattle or Lisbon)
We are looking for a Program Manager with an NLP background reporting to the Program Manager Lead in Lisbon. He/she will be responsible for helping managing customer deliverables, defining data quality, working with the Engineering team on the platform features, validating quality against customer requirements and data reporting and analytics. Scripting and basic programming skills will be required.
Required skills:
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BA or Masters in Linguistics or equivalent. Masters in Computational Linguistics is a plus.
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Experience in project management: gathering requirements; planning; resource allocation; communication with developers.
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Technical Skills: Python; Perl; Regular Expressions; SQL; Excel; Microsoft Office Suite;
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Proficiency in more than 2 languages.
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Proactivity, initiative, positive attitude, ability to solve problems creatively.
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Excellent speaking and writing skills in English.
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Good communication skills and ability to work with ambiguity.
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Able to work with flexible hours when the interactions with Seattle headquarters is needed or with overseas customers and partners.
Please submit your resume to hr@definedcrowd.com. We are looking forward to hearing from you.
Positions opened until filled. Date: 09/10/2016
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| 6-4 | (2016-10-20) Two Master research internships at LIMSI - CNRS, Orsay, France
Two Master research internships (with follow-up PhD scholarship) at LIMSI - CNRS, Orsay, France
Unsupervised Multimodal Character Identification in TV Series and Movies
Keywords : deep learning, speech processing, natural language processing, computer vision
Automatic character identification in multimedia videos is an extensive and challenging problem. Person identities can serve as foundation and building block for many higher level video analysis tasks, for example semantic indexing, search and retrieval, interaction analysis and video summarization. The goal of this project is to exploit textual, audio and video information to automatically identify characters in TV series and movies without requiring any manual annotation for training character models. A fully automatic and unsupervised approach is especially appealing when considering the huge amount of available multimedia data (and its growth rate). Text, audio and video provide complementary cues to the identity of a person, and thus allow to better identify a person than from either modality alone.
In this context, LIMSI (www.limsi.fr) proposes two projects, focusing on two different aspects of this multimodal problem. Depending on the outcome of the internship, both projects may lead to a PhD scholarship (one funding is already secured).
Project 1 ? natural language processing + speech processing
speaker A ? 'Nice to meet you, I am Leonard, and this is Sheldon. We live across the hall.'
speaker B ? 'Oh. Hi. I?m Penny.'
speaker A ? 'Sheldon, what the hell are you doing?'
speaker C ? I am not quite sure yet. I think I am on to something?
Just looking at these two short conversations, a human can easily infer that 'speaker A' is actually 'Leonard', 'speaker B' is Penny and 'speaker C' is Sheldon. The objective of this project is to combine natural language processing and speech processing to do the same automatically. Building blocks include automatic speech transcription, named entity detection, classification of names (first, second or third person) and speaker diarization. Preliminary works in this direction have already been published in [Bredin 2014] and [Haurilet 2016]
[Bredin 2014] Hervé Bredin, Antoine Laurent, Achintya Sarkar, Viet-Bac Le, Sophie Rosset, Claude Barras. Person Instance Graphs for Named Speaker Identification in TV Broadcast. Odyssey 2014, The Speaker and Language Recognition Workshop.
[Haurilet 2016] Monica-Laura Haurilet, Makarand Tapaswi, Ziad Al-Halah, Rainer Stiefelhagen. Naming TV Characters by Watching and Analyzing Dialogs. WACV 2016. IEEE Winter Conference on Applications of Computer Vision.
Project 2 ? speech processing + computer vision
This project aims at improving (acoustic) speaker diarization using the visual modality. Indeed, it was shown in a recent paper [Bredin 2016] that recent advances in deep learning for computer vision led to very reliable face clustering performance ? whereas speaker diarization is very bad at processing TV series and movies (mostly because current state of the art has not been designed to process this kind of content).
The first task is to design deep learning approaches (based on recurrent neural networks) to address talking-face detection (e.g. deciding, among all visible people, which one is currently speaking) by combining the audio and visual (e.g. lip motion) streams. The second task is to combine talking-face detection and face clustering to guide and improve speaker diarization (i.e. who speaks when?). Read [Bredin 2016] for more information on this kind of approach.
[Bredin 2016] Hervé Bredin, Grégory Gelly. Improving speaker diarization of TV series using talking-face detection and clustering. ACM Multimedia 2016, 24th ACM International Conference on Multimedia.
Profile: Master student in machine learning (experience in natural language processing, computer vision and/or speech processing is appreciated)
Location: LIMSI - CNRS, Orsay, France
Duration: 5/6 months
Salary: according to current regulations
Contact: Hervé Bredin (bredin@limsi.fr) with CV + cover letter + reference letter(s)
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| 6-5 | (2015-10-23) Vacancy for a one-year post-doctoral researcher position at the Radboud University, Nijmegen, The Netherlands
Vacancy for a one-year post-doctoral researcher position
at the Radboud University, Nijmegen, The Netherlands
*** Computational modelling of human spoken-word recognition ***
As a post-doctoral researcher, you will join the NWO-Vidi funded project
'Ignoring the merry in marry: The effect of individual differences in
attention and proficiency on non-native spoken-word recognition in noise'
headed by Dr. Odette Scharenborg. This project investigates the effect of
noise on non-native spoken-word recognition using a range of tasks tapping
into different processes underlying human spoken-word recognition, and the
effect of individual differences in attention and proficiency on
non-native spoken-word recognition in noise.
You will conduct research on or related to the computational modelling of
human spoken-word recognition. Research will focus on determining the best
method for the automatic classification of speech segments, building a
computational model of non-native human spoken-word recognition using Deep
Neural Networks, running simulations and comparing the model?s output with
existing human data. You will communicate your findings through papers in
peer-reviewed research journals and at international conferences.
What we expect:
- You hold a PhD in artificial intelligence, computer science,
computational (psycho)linguistics, or a related discipline
- You have a good knowledge of speech and human and/or automatic speech
processing
- You have experience with computational modelling
- You have experience with Deep Neural Networks
- You have a good command of spoken and written English
- You have multiple journal publications, two of which as a first author
- You are a team player who enjoys working with people from different
backgrounds
More information:
Odette Scharenborg
O.Scharenborg@let.ru.nl
http://odettescharenborg.ruhosting.nl
How to apply:
Please note: Only job applications uploaded via the university website are
taken into consideration. The job vacancy will soon become available via
http://www.ru.nl/werken/alle-vacatures/
The application should consists of:
-Motivation letter
-CV
-List of publications
-Names of two referents
Closing date: Sunday 20 November 2016.
Starting date: The preferred starting date is 1 January 2017.
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| 6-6 | (2016-11-02) PhD stipends at The Centre for Acoustic Signal Processing Research, Aalborg University, Denmark
PhD stipends at The Centre for Acoustic Signal Processing Research, Aalborg University, Denmark
The Centre for Acoustic Signal Processing Research (CASPR) will have a number of fully funded PhD stipends available in 2017.
We are looking for highly motivated, independent, and outstanding students that desire to do a successful 3-year PhD programme at Aalborg University. The ideal candidates must have strong expertise in one or more of the following disciplines: statistical signal processing, auditory perception, machine learning, information theory, or estimation theory. Good English verbal and written skills are a must. Excellent undergraduate and master degree grades are desired.
PhD positions in Denmark are fully funded, i.e. no tuition fees, and come with a salary. The salary is subject to a pay grade system based on prior working experience since completing your undergraduate degree. The yearly gross salary is in the range 41.500 ? 50.100 Euros.
You may obtain further information about the PhD stipends from Associate Professor Jan Østergaard (jo@es.aau.dk), Associate Professor Zheng-Hua Tan (zt@es.aau.dk), or Professor Jesper Jensen (jje@es.aau.dk), CASPR, Aalborg University, concerning the scientific aspects of the stipends.
Webpage for the positions: http://caspr.es.aau.dk/open-positions/
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| 6-7 | (2016-11-09) Internship on Search Engine Development at ELDA in Paris (France),
ELDA is opening an internship on Search Engine Development in Paris (France), starting on January 2017 for 6 months.
Profile
- MSc. in Computer Science;
- Basic knowledge in data structures and algorithms;
- Basic knowledge in Web applications architecture;
- Python and / or JavaScript language skills;
- Technical English
- Hands-on knowledge of a database system (ideally PostgreSQL);
- Knowledge of a search engine (Solr, Elasticsearch, Lucene) will be appreciated.
Duties
In the software development team at ELDA and under the supervision of an Engineer specialised in Natural Language Processing and Web Application Development, you will participate in the following tasks:
- produce a state-of-the-art overview on most powerful research engines that are currently available, such as Solr, Elasticsearch, or the full-text search features provided by current database systems, such as PostgreSQL.
- help specifying the full-text search needs for the LREC conference proceedings;
- help choosing the technical solution that best fits the context;
- participate in the design of a database structure (data schema) for the contents of the LREC proceedings web sites;
- harvest the LREC proceedings sites and populate the aforementioned database with all the relevant information extracted from the contents of the LREC proceedings sites;
- implement a search solution that is exhaustive, robust and works throughout all the LREC proceedings.
You will also participate in the regular meetings of the software development team at ELDA.
Application
This 6-month internship is based in Paris 13th district (Les Gobelins).
It should start in January 2017.
Applicants should email a cover letter addressing the points listed above together with a curriculum vitæ to Vladimir Popescu (vladimir@elda.org).
The internship is subject to a monthly allowance, commensurate with the candidate's educational qualifications and according to the French laws.
http://www.elra.info/en/opportunities/internships/
-*-*-*-*-*-*-*-
ELDA ('Evaluations and Language resources Distribution Agency') is a key player of the Human Language Technology domain. Operational body of ELRA, the European Language Resources Association, a European not-for-profit organisation promoting language resources in a European context, ELDA is in charge of executing a number of tasks on behalf of ELRA, including both the distribution and the production of Language Resources. Within the production projects, ELDA is often in the position of coordinating resource annotations, as well as performing quality control of these annotations.
Thus, ELDA supports the organization of ELRA?s biannual international scientific conference, LREC, the Language Resources and Evaluation Conference, which brings together, an increasing number (1200+) of top-tier researchers from all over the world, who submit and present scientific research articles.
In order to ease the navigation in this thesaurus of scientific articles, ELDA has set up a set of Web sites gathering the articles themselves, as well as the corresponding metadata (authors, titles, article abstracts, etc.).
In this context, ELDA wants to consolidate these sites, allowing the users to rely on a robust and exhaustive search throughout the article collections for all the editions of the LREC conference.
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| 6-8 | (2016-11-10) Offre de thèse LIMSI, Orsay, France
Sujet: offre de thèse en CIFRE, agents conversationnels
Sujet de thèse : Génération pour un agent conversationnel adaptatif
Directrices de thèse : Sophie Rosset et Anne-Laure Ligozat
Dans le cadre du projet Nanolifi (un agent conversationnel au service
de la ville, Le LIMSI recrute un.e doctorant.e en informatique.
L'objectif principal de la thèse est de modéliser des activités
dialogiques dans un cadre conversationnel humain-robot tout
venant. L'approche portera sur l'utilisation de méthodes à base
d'apprentissage non supervisé, par exemple de type zero-shot learning
ou par renforcement. La représentation sémantique et dialogique
manipulée devra intégrer des informations de type linguistique telles
que fournies par des outils de traitement automatique des langues
disponibles. La mesure d'évaluation devra prendre en compte la notion
d'engagement tant de l'utilisateur que du système. La base de
connaissances servant de support au cadre applicatif sera constitué à
partir des documents contenant l'information sur la ville.
Description complète
======================
http://www.adum.fr/as/ed/voirpropositionT.pl?site=PSaclay&matricule_prop=13069
Pré-requis
===========
* Master 2 en Informatique (ou équivalent), avec au moins une spécialité en
* Apprentissage
* Traitement automatique de la langue
* Traitement de la parole
Informations pratiques
=======================
* Début de thèse : début 2017
* inscription EDSTIC Univ. Paris Saclay
* Financement CIFRE
Candidature
=============
Dossier de candidature : lettre de motivation, CV, résultats de Licence et master,
Copie à Sophie Rosset (rosset@limsi.fr), Anne-Laure Ligozat (annlor@limsi.fr)
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| 6-9 | (2016-11-19) Stage de master recherche dans le cadre du projet ANR Vocadom, Grenoble, France
L'équipe GETALP du LIG propose un stage de master recherche dans le cadre du projet ANR Vocadom.
--
2016-2017 M2R INFO GETALP
Stage M2R Informatique
Commande vocale à large vocabulaire pour la domotique par apprentissage profond et “word embedding”
Large vocabulary voice command recognition for automation of the home using deep learning and word embedding
Responsable(s) : Michel Vacher, François Portet, Benjamin Lecouteux
Mots-clés : automatic speech recognition, Deep Neural Network, Word Embedding, Home Automation, reconnaissance automatique de Parole, apprentissage profond, représentation vectorielle des mots, domotique
Durée du stage : 5 mois, possibilité de continuer en thèse (financement ANR)
Lieu du stage : équipe GETALP du LIG, bâtiment IMAG, Domaine universitaire de Grenoble
Contexte du stage
Dans le cadre du projet VOCADOM financé par l’ANR, nous cherchons à concevoir un système domotique contrôlé à distance par la voix et qui peut être utilisé dans des conditions réelles (bruit, présence de plusieurs personnes). Outre les problèmes de bruit, l’un des défis à relever est de constamment s’adapter aux utilisateurs finaux en prenant en compte de toute l’information disponible (capteurs audio et capteurs domotiques). L’une de ces adaptions consiste à comprendre l’énoncé de parole des utilisateurs sans
avoir de contrainte sur le vocabulaire et la syntaxe que les utilisateurs utiliseront. Par exemple, l’ordre standard pour allumer la lumière est “Nestor allume la lumière”, mais il pourrait être “Nestor, on n’y voit rien” ou «S’il te plaît Nestor allume la lumière “.
Sujet de stage
Le but de ce stage est de permettre cette association “énoncé vocal” <-> “ordre domotique” avec le minimum de contraintes lexicales et syntaxiques. Des études précédentes ont utilisé une distance phonétique de Levenshtein qui est bien adaptée lorsque les prononciations sont proches. Par conséquent, nous proposons d’opérer non seulement au niveau phonétique mais également au niveau lexical en travaillant sur le treillis de sortie du décodeur du système de reconnaissance vocale.
Le travail proposé consistera en une première étape pour faire une étude bibliographique liées à ce domaine. La deuxième étape consistera à développer une méthode pour explorer le réseau à la sortie du décodeur KALDI ASR. Celle-ci utilisera un modèle externe de mots basés sur le Word Embedding (acquis par apprentissage profond) afin de pouvoir associer un score de proximité entre un énoncé connu et un nouvel énoncé. Le système développé sera ensuite évalué sur un corpus synthétique (construit par synthèse vocale) et sur le corpus enregistré dans l’habitat intelligent du laboratoire.
L’étudiant recruté pourra bénéficier des études antérieures, dans lesquelles les enregistrements ont été faits dans un véritable appartement avec plusieurs chambres, chacune équipée de micros. Les participants y ont joué des scénarios réalistes de la vie quotidienne (AVQ) (dormir, se lever, faire sa toilette, préparer un repas, déjeuner, se détendre, sortir …). Cela nous a permis de recueillir un corpus réaliste contenant les commandes vocales enregistrées dans diverses conditions.
Compétences souhaitées : langage C++, bonne maîtrise de la langue française
Références
M. Vacher, S. Caffiau, F. Portet, B. Meillon, C. Roux, E. Elias, B. Lecouteux, P. Chahuara (2015). “Evaluation of a context-aware voice interface for Ambient Assisted Living: qualitative user study vs. quantitative system evaluation“, ACM – Transactions on Speech and Language Processing, Association for Computing Machinery, 2015, Special Issue on Speech and Language Processing for AT (Part 3), 7 (issue 2), pp.5:1-5:36.
D. Povey, L. Burget, M. Agarwal, P. Akyazi, F. Kai, A. Ghoshal, O. Glembek, N. Goel, M. Karafiát, A. Rastrow, R. C. Rose, P. Schwarz, and S. Thomas (2011). “The subspace gaussian mixture model—a structured model for speech recognition“, Computer Speech & Language, vol. 25, no. 2, pp. 404 – 439.
Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). “A neural probabilistic language model“. Journal of machine learning research, 3(Feb), 1137-1155.
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Michel VACHER Ingénieur de Recherche CNRS - HDR - Directeur adjoint Institut Carnot LSI http://www.carnot-lsi.com - Laboratoire d'Informatique de Grenoble - LIG - UMR 5217 Groupe d'Etude en Traduction/Traitement Automatique des Langues et de la Parole 700 avenue Centrale - Bâtiment IMAG - Bureau 330 Domaine Universitaire - 38401 St Martin d'Hères URL labo : http://www.liglab.fr/ perso : http://lig-membres.imag.fr/vacher/ Mel. : Michel.Vacher@imag.fr | tel: 0033 (0)4 57 42 14 38 Adresse postale : LIG - Bâtiment IMAG - CS 40700 - 38058 GRENOBLE CEDEX 9
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| 6-10 | (2016-11-20) 3 subjets Master Recherche 2 in NLP at Loria Nancy for 2016-2017
3 subjets Master Recherche 2 in NLP at Loria Nancy for 2016-2017:
Subject 1
Title: Data selection for the training of deep neural networks in the framework of automatic speech recognition
Supervisor: Irina Illina
Team and lab: MultiSpeech
Contact: illina@loria.fr
Co-supervisor: Dominique Fohr
Team and lab: MultiSpeech
Contact : dominique.fohr@loria.fr
Motivations and context
More and more audio/video appear on Internet each day. About 300 hours of multimedia are uploaded per minute. Nobody is able to go thought this quantity of data. In these multimedia sources, audio data represents a very important part. Classical approach for spoken content retrieval from audio documents is an automatic speech recognition followed by a text retrieval. In this internship, we will focus on the speech recognition system.
One of the important modules of an automatic speech recognition system is the acoustic model: it models the sounds of speech, mainly phonemes. Currently, the best performing models are based on deep neural networks. These models are trained on a very large amount of audio data, because the models contain millions of parameters to estimate. For training acoustic models, it is necessary to have audio documents for which the exact text transcript is available (supervised training).
The Multi-Genre Broadcast (MGB) Challenge is an evaluation of speech recognition systems, using TV recordings in English or Arabic. The speech data is broad and multi-genre, spanning the whole range of TV output, and represents a challenging task for speech technology. Speech Data cover the multiple genres in broadcast TV, categorized in terms of 8 genres: advice, children?s, comedy, competition, documentary, drama, events and news.
The problem with MGB data challenge is that the exact transcription of audio documents is not available. Only subtitles of TV recordings are given. These subtitles are sometimes far from what is actually pronounced: some words may be omitted, hesitations are rarely transcribed and some sentences are reformulated.
In this internship, we will focus on the problem of data selection for efficient acoustic model training.
Objectives
A subtitle is composed of a text, a start time appearance (timecode) on the screen and an end time of appearance. These start and end times are given relative to the beginning of the program. It is easy to associate subtitle and the corresponding audio segment.
We have at our disposal a very large audio corpus with the corresponding subtitles and we want to develop data selection methods for obtaining high performance acoustic models. That is to say with a word error rate as small as possible. If we use all the training data, the errors in the subtitles will lead to poor quality acoustic models and therefore a high recognition word error rate.
We propose to use a deep neural network (DNN) to classify the segments into two categories: audio segments corresponding to subtitles and audio segments not corresponding to subtitles. The student will analyze what information, acoustic and/or linguistic, is relevant to this selection task and can be used as input of the DNN.
The student will validate the proposed approaches using the automatic transcription system of TV broadcast developed in our team.
Required skills background in statistics, natural language processing and computer program skills (Perl, Python).
Localization and contacts: Loria laboratory, Speech team, Nancy, France
irina.illina@loria.fr dominique.fohr@loria.fr
Candidates should email a detailed CV with diploma
===========================================================================
Subject 2
Title: Domain adaptation of neural network language model for speech recognition
Supervisor: Irina Illina
Team and lab: MultiSpeech
Contact: illina@loria.fr
Co-supervisor: Dominique Fohr
Team and lab: MultiSpeech
Contact : dominique.fohr@loria.fr
Motivation and Context
Language models (LMs) play a key role in modern automatic speech recognition systems and ensure that the output respects the pattern of the language. In the state-of-the-art systems, the language model is a combination of n-gram LMs and neural network LMs because they are complementary. These LM are trained on huge text corpora.
The language models are trained on a corpus of varied texts, which provides average performance on all types of data. However, document content is generally heavily influenced by the domain, which can include topic, genre (documentary, news, etc.) and speaking style. It has been shown that domain adaptation of LMs to small amounts of matched in-domain text data provide significant improvements in both perplexity and word error rate. The objective of the internship is to adapt a neural networks based language model to the domain of an audio document to be recognized. For this, we will use a small specific text corpus.
The Multi-Genre Broadcast (MGB) Challenge is an evaluation campaign of speech recognition systems, using TV recordings in English or Arabic. The speech data is broad and multi-genre, spanning the whole range of TV output, and represents a challenging task for speech technology. Speech data covers the multiple genres in broadcast TV, categorized in terms of 8 genres: advice, children?s, comedy, competition, documentary, drama, events and news.
During the internship, the student will develop LM adaptation methods in the context of the MGB data.
Goals and Objectives
Neural network LM adaptation can be categorized as either feature-based or model-based.
In the feature-based adaptation, the input of the neural network is augmented with auxiliary features, which model domain, topic information, etc. However, these auxiliary features must be learn during the training of the LM model and thus require whole model retraining.
Model-based adaptation consists in adding complementary layers and training these layers with domain-specific adaptation data. An advantage of this method is that full retraining is not necessary. Another model-based adaptation method is fine-tuning: after training the model with the whole training data, the model is tuned with the target domain data. The downside of this approach is the lack of the optimization objective.
During the internship, the student will perform a bibliographic study on model adaptation approaches. Depending on the pros and cons of these approaches, we will propose a method specific to MGB data. This method may include changing the architecture of the neural network.
The student will validate the proposed approaches using the automatic transcription system of radio broadcast developed in our team.
Required skills background in statistics, natural language processing and computer program skills (Perl, Python).
Localization and contacts: Loria laboratory, Speech team, Nancy, France
irina.illina@loria.fr dominique.fohr@loria.fr
Candidates should email a detailed CV with diploma
===========================================================================
Subject 3
Title: Using Wikipedia to search for proper names relevant to the audio document transcription
Supervisor: Irina Illina
Team and lab: MultiSpeech
Contact: illina@loria.fr
Co-supervisor: Dominique Fohr
Team and lab: MultiSpeech
Contact : dominique.fohr@loria.fr
Motivation and Context
More and more audio/video appear on Internet each day. About 300 hours of multimedia are uploaded per minute. Nobody is able to go thought this quantity of data. In these multimedia sources, audio data represents a very important part. Classical approach for spoken content retrieval from audio documents is an automatic speech recognition followed by a text retrieval.
An automatic speech recognition system uses a lexicon containing the most frequent words of the language and only the words of the lexicon can be recognized by the system. New proper names (PNs) appear constantly, requiring dynamic updates of the lexicons used by the speech recognition system. These PNs evolve over time and no vocabulary will ever contains all existing PNs. These missing proper names can be very important for the understanding of the test document.
In this study, we will focus on the problem of proper names in automatic recognition systems. The problem is to find relevant proper names for the audio document we want to transcribe. For this task, we will use a remarkable source of information: Wikipedia, free online encyclopedia, the largest and most popular reference work on the internet. Wikipedia contains a huge number of proper names.
Goals and Objectives
We assume that in an audio document to transcribe we have missing proper names, i.e. proper names that are pronounced in the audio document but that are not in the lexicon of the automatic speech recognition system; these proper names cannot be recognized (out-of-vocabulary proper names, OOV PNs)
The goal of this internship is to find a list of relevant OOV PNs that correspond to an audio document. We will use Wikipedia as a source of potential proper names.
Assuming that we have an approximate transcription of the audio document and a Wikipedia dump, two main points will be addressed:
- How to represent a Wikipedia page and in which space? One possibility is to use word embeddings (for instance Mikolov?s word2vec ).
- Using the previous representation, how to select relevant pages according to the approximate transcription of the audio document? The automatic speech recognition systems of broadcast news have a 10-20% word error rate. If we project documents in a continuous space, different distances can be studied.
In a first step, we can consider Wikipedia page as a simple text page. In a second step, the student should use the structure of the Wikipedia pages (page links, tables, headings, infobox).
During the internship, the student will investigate methodologies based on deep neural networks.
The student will validate the proposed approaches using the automatic transcription system of radio broadcast developed in our team.
Required skills background in statistics, natural language processing and computer program skills (Perl, Python).
Localization and contacts: Loria laboratory, Speech team, Nancy, France
irina.illina@loria.fr dominique.fohr@loria.fr
Candidates should email a detailed CV with diploma
===========================================================================
--
Dominique Fohr
Research scientist CNRS
LORIA-INRIA MultiSpeech Group
Address: Bâtiment LORIA, BP 239, F54506 Vandoeuvre, France
Email: dominique.fohr@loria.fr Tel: (+33) 3.83.59.20.27
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| 6-11 | (2016-12-02) Tenure senior research position at INRIA Bordeaux France
Flowers Lab at Inria (Bordeaux, France)
Deadline: 16th december
We are searching highly qualilified candidates for a tenure senior research position (full time research, no teaching mandatory).
Candidates should have an outstanding academic track record in one or two of the following domains:
1) Computational modelling of cognitive development, including the following research topics and methods:
- Models of exploration and active learning in humans and animals
- Models of human reinforcement learning and decision making
- Bayesian or neuronal models of development
- Models of autonomous lifelong learning
- Models of tool learning
- Sensorimotor, language and social development
- Strong experience of collaborations with developmental psychologists or neuroscientists
2) Lifelong autonomous machine learning and artificial intelligence, including:
- Unsupervised deep reinforcement learning
- Intrinsic motivation
- Developmental learning, curriculum learning
- Contextual bandit algorithms
- Multitask and transfer learning
- Hierarchical learning
- Strong experience in benchmarking with robotic or virtual world setups
As the Flowers lab domains of application are robotics/HRI/HCI and educational technologies, experience in one of these two
domains would be a clear asset.
Experience in writing successful grant proposals will also be considered positively.
Candidates should have a strong postdoc experience after their PhD, or may already be at the level of occupying a research position
in a university or research organization.
The Flowers Lab: developmental robotics and lifelong multitask machine learning
===================================================================
The Flowers Lab, headed by Pierre-Yves Oudeyer, gathers a team of ~20 members and has been one of the pioneers of developmental robotics and lifelong machine learning
and artificial intelligence in the last decade, in particular through developping models of intrinsically motivated learning of repertoires of skills that have both contributed
to advance understanding of human curiosity and development, and to advance incremental online multitask machine learning techniques in difficult high-dimensional
robotic spaces.
This work in the Flowers lab are conducted in the context of large international projects (e.g. ERC grant, European projects 3rdHand and DREAM, HFSP project Neurocuriosity),
with interdisicplinary collaborations with other labs in neuroscience, psychology, machine learning and robotics. The successful candidates would be directly
involved in these international collaborations.
The Flowers Lab has is also developping applications of these concepts and techniques in the domain of educational technologies, including adaptive
intelligent tutoring systems (using bandit algorithms), educational robotics, and software that stimulate curiosity and learning in humans.
The Flowers lab has recently spin-off the Pollen Robotics startup company, and is involved in multiple collaborations with industrials through Inria's strong
support towards impacting both science and industry.
Inria and EnstaParistech
===================
The lab is within Inria, which is a prestigious, and also the largest, public European research insitution focused on computer science, mathematics and their applications.
Inria's teams and researchers (> 2800 employees) have received prestigious awards, coordinate many international projects, and have created strong innovations now used in many
parts of industry. Inria research center in Bordeaux gathers around 300 researchers.
The Flowers Lab is also associated to EnstaParisTech, which is a prestigious French engineering school (university).
Bordeaux
========
The Flowers lab in Bordeaux is located in a great building on the border of one of the world most famous vineyard, and 10mn by tram from Bordeaux town center
Web
===
How to apply
===========
Then, a pre-selection will be made and a full application with a detailed research statement and program will have to be submitted early february.
The successful candidate would begin to work between september and december 2017.
Pierre-Yves Oudeyer
Research director, Inria
Head of Flowers Lab
Inria and Ensta ParisTech
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| 6-12 | (2016-12-19) Postdoc at LIMSI, Orsay, France
LIMSI offers a one-year postdoctoral position on unsupervised identification of characters in TV series.
--- Keywords ---
Deep learning, speech processing, natural language processing, computer vision
--- Project summary ---
Automatic character identification in multimedia videos is an extensive and challenging problem. Person identification serves as foundation and building block for many higher level video analysis tasks, for example semantic indexing, search and retrieval, interaction analysis and video summarization.
The goal of this project is to exploit textual, audio and video information to automatically identify characters in TV series and movies without requiring any manual annotation for training character models. A fully automatic and unsupervised approach is especially appealing when considering the huge amount and growth of available multimedia data. Text, audio and video provide complementary cues to the identity of a person, and thus allow to better identify a person than from either modality alone.
To this end, we will address three main research questions: unsupervised clustering of speech turns (i.e. speaker diarization) and face tracks in order to group similar tracks of the same person without prior labels or models; unsupervised identification by propagation of automatically generated weak labels from various sources of information (such as subtitles and speech transcripts); and multimodal fusion of acoustic, visual and textual cues at various levels of the identification pipeline.
While there exist many generic approaches to unsupervised clustering , they are not adapted to heterogeneous audiovisual data (face tracks vs. speech turns) and do not perform as well on challenging TV series and movies content as they do on other controlled data. Our general approach is therefore to first overcluster the data and make sure that clusters remain pure , before assigning names to these clusters in a second step. On top of domain specific improvements for either modality alone, we expect joint multimodal clustering to take advantage of three modalities and improve clustering performance over each modality alone.
Then, unsupervised identification aims at assigning character names to clusters in a completely automatic manner (i.e. using only available information already present in the speech and video ). In TV series and movies, character names are usually introduced and reiterated throughout the video. We will detect and use addresser/addressee relationships in both speech transcripts (using named entity detection techniques) and video (using mouth movements, viewing direction and focus of attention of faces). This allows to assign names to some clusters, learn discriminative models and assign names to the remaining clusters.
For evaluation, we will extend and further annotate a corpus of four TV series (57 episodes) and one movie series (8 movies), a total of about 50 hours of video. This diverse data covers different filming styles, type of stories and challenges contained in both video and audio. We will evaluate the different steps of this project on this corpus, and also make our annotations publicly available for other researchers working in the field.
---
--- Information ---
PhD in machine learning (experience in natural language processing, computer vision and/or speech processing is appreciated)
Location: LIMSI - CNRS, Orsay, France
Duration: 12 months, starting date to be defined with the selected candidate
Contact: Hervé Bredin (bredin@limsi.fr)
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| 6-13 | (2017-01-12) Internship INA Paris: Segmentation Parole/Musique de documents multimédias à l’aide de réseaux de neurones profonds
Segmentation Parole/Musique de documents multimédias à
l’aide de réseaux de neurones profonds
Stage de fin d’études d’Ingénieur ou de Master 2 – 2016-2017
Mots clés: Deep Learning, Segmentation Audio, Machine Learning, Music Information
Retrieval, Open Data
Contexte
Les missions de l’institut national de l’audiovisuel (Ina) consistent à archiver et à valoriser la
mémoire audio-visuelle française (radio, télévision et médias Web). A ce jour, plus de 15 millions
d’heures de documents télé et radio sont conservés, dont 1,5 millions d’heures numérisées. En
raison de la masse de données considérée, il n’est techniquement pas possible de procéder à une
description manuelle, systématique et détaillée de l’ensemble des archives. Il est donc nécessaire
d’utiliser des techniques d’analyse automatique du contenu pour optimiser l’exploitation de cette
masse de données.
Objectifs du stage
La segmentation Parole/Musique (SPM) consiste à segmenter un flux audio en zones homogènes de
parole et de musique. Cette étape est nécessaire en amont de tâches d’indexation haut niveau, telles
que la reconnaissance de la parole, du locuteur, du morceau ou du genre musical. Pour ces
différentes raisons, cette tâche a suscité beaucoup d’intérêts au sein des communautés de traitement
de la parole, ainsi qu’en indexation musicale.
L’utilisation de systèmes de SPM à l’Ina répond à trois cas d’usage principaux. En premier lieu, il
s’agit de localiser rapidement les zones d’intérêt au sein des médias, pour fluidifier les processus de
description des archives, réalisés manuellement par des documentalistes. La description manuelle
des archives est coûteuse, et réalisée avec un niveau de détail variable: les journaux télévisés étant
décrits plus finement que les fonds radio anciens. Les systèmes SPM peuvent ainsi permettre de
faciliter la navigation dans des fonds d’archives sous-documentés. Le dernier cas d’usage
correspond à la segmentation en morceaux de musique: consistant à détecter le début et la fin des
morceaux. Cette tâche permet de mesurer la durée des extraits musicaux présents dans les archives,
et ainsi rémunérer les sociétés d’auteurs concernées lorsque les archives sont commercialisées.
A ce jour, un certain nombre de situations restent difficiles pour les systèmes SMS. Il s’agit
notamment la différentiation entre voix parlée et voix chantée, notament dans certains styles
musicaux où les propriétés spectrales de la voix chantée et parlée sont similaires. Une autre
difficulté rencontrée est liée aux cas où la parole est superposée à la musique, ce qui arrive assez
fréquemment dans les émissions radio et télé. Une autre difficulté rencontrée par les systèmes
actuels est la liée à la finesse de la segmentation temporelle, généralement de l’ordre de la seconde.
L’objectif du stage consiste à concevoir des systèmes basés sur l’utilisation de réseaux de neurones
profonds pour la segmentation parole/musique d’archives audio-visuelles. Les méthodes proposées
devront prendre en charge la diversité des archives de l’Ina (archives radio des années 1930 à nos
jours). Une partie du stage sera consacrée à l’analyse des corpus existants, et à la constitution d’un
corpus annoté (interprète, morceau, genre, locuteur, ...) permettant d’avoir un maximum de contrôle
sur l’ensemble des paramètres testés lors des évaluations. L’autre partie du stage sera consacré à la
mise au point d’architectures basées sur des réseaux de neurones profonds pour la SPM, qui sera
réalisée dans la continuité des travaux en cours utilisant des réseaux de neurones convolutionnels.
Le langage de programmation utilisé dans le cadre de ce stage sera Python. Le stagiaire aura accès
aux ressources de calcul de l’Ina (cluster et serveurs GPU).
Conditions du stage
Le stage se déroulera sur une période de 6 mois, au sein de l’équipe recherche de l’Ina. Il aura lieu
sur le site Bry2, situé au 18 Avenue des frères Lumière, 94366 Bry-sur-Marne. Le stagiaire sera
encadré par David Doukhan (ddoukhan@ina.fr) et Jean Carrive (jcarrive@ina.fr), et percevra une
rémunération mensuelle de 527,75 euros/mois.
Bibliographie
Jimena, R. L., Hennequin, R., & Moussallam, M. (2015). Detection and characterization of singing
voice using deep neural networks.
Peeters, G. (2007). A generic system for audio indexing: Application to speech/music segmentation
and music genre recognition. In Proc. DAFX (Vol. 7, pp. 205-212).
Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox, D. D. (2009). A high-throughput screening approach
to discovering good forms of biologically inspired visual representation. PLoS Comput Biol, 5(11),
e1000579.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
|
| 6-14 | (2017-01-15) Postdoctoral Positions in Linguistics/Sciences du Langage, LPL, CNRS, Aix-Marseille, France
Call for Postdoctoral Positions in Linguistics/Sciences du Langage
Institution: Laboratoire Parole et Langage (CNRS, Aix-Marseille Université)
Location: Aix-en-Provence, France
No. of positions: 2
Duration: 18-24 months
Application deadline: 1 March 2017
The Laboratoire Parole et Langage (CNRS, Aix-Marseille Université) invites applications for two postdoctoral positions to be supported by a grant from the A*MIDEX Foundation. The funded project explores the relationship between social variables and linguistic representation, and seeks to develop and extend an explicit cognitive model that accounts for the effects of socio-indexical cues on production and perception. The empirical basis for the project involves a combination of experimentation (production, perception, ERP), corpus analysis, and computational modeling. The selected applicants will engage with an interdisciplinary team from the Institute of Language, Communication, and the Brain which includes experts in neuroscience, psychology, computer science, and mathematics, among other areas.
A special emphasis of the project concerns issues in prosody and intonation, so an interest in, or experience with, prosody is highly desirable. In addition, the ideal applicant will have experience in one or more of the following areas:
Sociolinguistics (quantitative)
Machine learning and statistical modeling (esp. sound structure/phonetics)
Design and analysis of speech corpora
Prosody and meaning (esp. information structure)
Knowledge of French is not essential. Each postdoctoral appointment is for approximately 18-24 months depending on the starting date. Candidates must have completed all PhD requirements before the starting date. The starting date is flexible, though the position should be filled by 1 June 2017.
Applications should include (i) a cover letter that relates the applicants’ experience and interests to the project, (ii) a comprehensive CV, (iii) a PDF copy of all publications or a list of links where these can be accessed, and (iv) the names and contact information of at least two references.
Applications in French or English may be sent by email to Oriana Reid-Collins at oriana.reid-collins@lpl-aix.fr.
For further inquiries regarding the position or the project, please contact James Sneed German (principal investigator) at james.german@lpl-aix.fr.
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| 6-15 | (2017-01-20) Postdoc in Mental Health, Affective Computing and Machine Learning at CMU, Pittsburgh, PA, USA
Postdoc in Mental Health, Affective Computing and Machine Learning
Carnegie Mellon University, School of Computer Science
University of Pittsburg, School of Medicine
*** US citizenship or a green card is required to be eligible for consideration. ***
The Multimodal Communication and Machine Learning Laboratory (MultiComp Lab) at Carnegie Mellon University is seeking creative and energetic applicants for a two-year postdoctoral position. This opportunity is part of NIMH-funded training program based at the University of Pittsburg School of Medicine. The position includes a competitive salary with full benefits and travel resources.
Using recent progress in machine learning and artificial intelligence, the postdoc will study patient’s multimodal behaviors (verbal, visual and vocal) during semi-structured clinical interviews to identify behavior markers of mental health disorders (e.g., depression, schizophrenia, suicidal ideation). The postdoc will work under the supervision of Dr. Louis-Philippe Morency (CMU MultiComp Lab’s director), in collaboration with clinicians and researchers at University of Pittsburgh’s Western Psychiatric Institute and Clinic.
The successful applicant will have an extensive research experience in automatic multimodal behavior analysis in the mental health domain, including facial and gesture analysis, acoustic signal processing, linguistic computation and multimodal machine learning.
Required
- PhD in computer science or mental-health related field (at the time of hire)
- Research experience in human behavior analysis, affective computing and machine learning
- US citizenship or a green card is required to be eligible for consideration
Desired
- Publications in top machine learning, speech processing and/or computer vision conferences and journals.
- Research involving clinical patients with mental health disorders (e.g., depression, schizophrenia, suicidal ideation)
- Experience mentoring graduate and undergraduate students
Job details
- Preferred start date: May 1st, 2017 (negotiable)
- Candidate will work under the supervision of Dr. Louis-Philippe Morency, MultiComp Lab’s director, at CMU Pittsburgh campus.
- Research will be performed in collaboration with clinicians and researchers at University of Pittsburgh’s Western Psychiatric Institute and Clinic.
- Competitive salary with full benefits and travel resources.
How to apply
- Email applications should be sent to morency@cs.cmu.edu with the title “Postdoc application – NIMH program”
- A single PDF file titled FirstNameLastName.pdfshould be attached to the email, including:
- a brief cover letter (with expected date of availability),
- a CV including list of publications and email addresses of 3 references,
- two representative publications (including full citation information)
=====================
Louis-Philippe Morency
Assistant Professor
School of Computer Science
Carnegie Mellon University
Multimodal Commination and Machine Learning Laboratory
https://www.cs.cmu.edu/~morency/
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| 6-16 | (2017-01-21) Internship at INRIA Bordeaux, France
Stage de 6 mois pour étudiants en Master2 à INRIA Bordeaux
Titre du Sujet de Stage :Analyse de la parole pour le diagnostic différentiel entre maladie de Parkinson et l'atrophie multisystématisée
Description :
La maladie de Parkinson (MP) et l'atrophie multisystématisée (AMS) sont des maladies neurodégénératives. La dernière appartient au groupe des troubles parkinsoniens atypiques et est responsable d?un pronostic péjoratif. Dans les premiers stades de la maladie, les symptômes de MP et AMS sont très similaires, surtout pour AMS-P où le syndrome parkinsonien prédomine. Le diagnostic différentiel entre AMP-P et MP peut être très difficile dans les stades précoces de la maladie, tandis que la certitude de diagnostic précoce est important pour le patient en raison du pronostic divergent. En effet, malgré des efforts récents, aucun marqueur objectif valide n'est actuellement disponible pour guider le clinicien dans ce diagnostic différentiel. La nécessité de ces marqueurs est donc très élevé dans la communauté de la neurologie, en particulier compte tenu de la gravité du pronostic de AMS-P.
Les troubles de la parole, communément appelés dysarthrie, sont un symptôme précoce commun aux deux maladies et d'origine différente. Notre approche consiste à utiliser la dysarthrie, grâce à un traitement numérique des enregistrements vocaux des patients, comme un vecteur pour distinguer entre MP et AMS-P dans les stades précoces de la maladie.
L'objectif du stage est d'utiliser des techniques connues de mesure de perturbation de la voix ainsi que des techniques récemment développées par l'équipe GeoStat d'Inria pour faire une étude expérimentale préliminaire sur le pouvoir discriminant de ces différentes mesures. Cette études se fera sur des bases de données médicales existantes.
Le stage déboucherait sur une bourse de thèse de doctorat dans le cadre d'une allocation ANR qui finance ce projet de recherche. Les partenaires cliniques de ce projet sont des centres du CHU-Bordeaux et du CHU-Toulouse de renommée internationale sur MP et AMS.
Responsable du stage :Dr. Khalid Daoudi, équipe GeoStat (khalid.daoudi@inria.fr).
Lieu du stage : INRIA- Bordeaux Sud Ouest (http://www.inria.fr/bordeaux). Bordeaux, France.
Durée du Stage : 6 mois
Rémunération : 500euros/mois
Connaissances requises : De bonnes connaissances en traitement de la parole/signal ainsi qu'en programmation C++ et Matlab sont nécessaires. Des connaissances en apprentissage statistique (Machine learning) seraient un grand plus.
Les candidatures doivent être adressées à khalid.daoudi@inria.fr
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| 6-17 | (2017-01-22) Stage de 6 mois au LIA Avignon, France
Stage de 6 mois au LIA Avignon
Le stage que nous proposons se situe dans le cadre d?une collaboration entre la société MediaWen International et le Laboratoire d?Informatique d?Avignon (LIA). MediaWen propose des solutions en ligne pour le sous-titrage, la traduction et le doublage de vidéo sur le web. Une plate-forme de travail collaboratif inclut les différentes briques technologiques et permet d?accélérer ou d?automatiser les différents traitements.
Dans ce cadre, MediaWen et le LIA souhaitent explorer la faisabilité et l?intérêt d?une brique technologique autour de la détection automatique de la langue parlée. Les deux originalités majeures seront de pouvoir ajouter facilement une langue à partir d?un ensemble réduit d?exemples audio et de définir une stratégie interactive dans laquelle le critère à optimiser est le temps de travail de l?opérateur (à qualité de production identique).
L?objectif du stage proposé est de mettre en place cette brique en se basant sur la plate-forme ALIZE (développé en C++) qui a déjà donné lieu à plusieurs implémentations de systèmes de reconnaissance de la langue parlée.Une solution basée sur le paradigme des i-Vectors sera choisie. L?approche retenue sera dans un premier temps développée et testée en utilisant des données internes du laboratoire (notamment les données NIST) et en simulant les réponses de l?opérateur. Elle sera ensuite intégréedans les outils de MediaWen et testée sur les données correspondantes.
Une poursuite en thèse est envisageable selon le degré de la réussite de ce stage.
Le stagiaire sera encadré au LIA par Driss MATROUF (MCF-HDR) et Jean-François BONASTRE (Professeur). Il bénéficiera du soutien des spécialistes de MediaWen, pleinement associés au déroulé de ce stage.
Profil et Niveau :
Master en informatique, mathématiques ou traitement du signal. Un bon niveau en développement logiciel, dont la connaissance de C++, est requis.
Motivation, curiosité scientifique et rigueur seront des qualités demandées.
Durée :
5 à 6 mois (prolongation possible)
Rémunération :
~530 Euros/mois (indemnités légales pour un stage de niveau Master)
Contact:
driss.matrouf@univ-avignon.fr
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| 6-18 | (2017-01-27) Two R and D engineers at INRIA Nancy France
Our team at Inria Nancy is recruiting two R&D engineers for an ambitious industrial
project on voice processing in audiovisual contents. The mission is to develop, evaluate,
and improve software prototypes based on the latest scientific advances and transfer them
to the partner software development company and the sound creation studio which initiated
the project. The resulting commercial software will be a reference in the professional
audiovisual field.
*R&D engineer A*
Start date: May 2017
Duration: 18 months
Missions:
- speaker recognition based on ALIZE
- speech enhancement by multichannel deep learning
Profile:
- MSc or PhD in computer science, signal processing, or machine learning
- operational skills in software engineering (version control, tests, software quality)
and Python 3 language (numpy, scipy, Keras)
- experience in speaker recognition or speech enhancement would be a plus
Salary: 2048 to 2509? net/month depending on experience
*R&D engineer B*
Start date: May-June 2017
Duration: 16 months
Missions:
- speech recognition based on Kaldi
- concatenative speech synthesis
Profile:
- MSc or PhD in computer science, signal processing, or machine learning
- operational skills in software engineering (version control, tests, software quality)
and Python 3 (numpy, scipy, Keras) and Java (Java SE 8) languages
- experience in speech recognition or synthesis would be a plus
Salary: 2048 to 2509? net/month depending on experience
*Work environment*
Nancy is one of the top cities for young engineers in France with cheap accomodation, a
vibrant cultural scene, and good connections to Paris (1.5h), Luxemburg (1.5h), Belgium,
and Germany. Inria Nancy is a 500-people research institute dedicated to computer
science. The Multispeech team (https://team.inria.fr/multispeech/) is a 30-people group
covering various fields of speech science, with a strong emphasis on machine learning and
signal processing.
*To apply*
Send a CV, a motivation letter and 1 to 3 optional recommendation letters to
emmanuel.vincent@inria.fr. Mention which position(s) you are applying for. Applications
will be assessed on a rolling basis until March 17. Please apply as soon as possible
before that date.
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| 6-19 | (2017-02-01) Maitre de conférences, Ecole centrale Marseille France
L'Ecole Centrale Marseille ouvre un poste de Maitre de Conférences en
Informatique au concours 2017 dont le profil enseignement et recherche
est précisé ci-dessous (poste en cours de publication).
Liens utiles :
Ecole Centrale Marseille https://www.centrale-marseille.fr/
Laboratoire d?Informatique Fondamentale : http://www.lif.univ-mrs.fr/
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Profil de poste MC en informatique à l?ECM
- Enseignement
Le/la maître de conférence recruté(e) devra être capable d?assurer un
enseignement attractif pour des élèves ingénieurs généralistes.
Il/elle aura vocation à s'intégrer au sein de l'équipe pédagogique
informatique pour assurer des enseignements de tronc commun
(Algorithmie, modélisation objet, stockage et traitement des données),
participer aux enseignants en informatique dans les options de
deuxième et troisième année, s?investir dans des proposer des projets
et suivre des groupes d'étudiants tout au long de leurs réalisations,
mais également participer à des actions de formation continue ou en
alternance. Il/elle jouera un rôle dans l'animation, la coordination
et l'évolution des enseignements, et participera aux actions
transverses multidisciplinaires de l'École Centrale Marseille.
Contacts : Pascal Préa (pascal.prea@centrale-marseille.fr)
- Recherche :
Le/la candidate devra en priorité développer des recherches dans le
cadre de projets initiés entre le Laboratoire d?Informatique
Fondamentale de Marseille (LIF) et l?Ecole Centrale Marseille (ECM).
Ces deux projets ont pour thèmes d?une part le traitement de données
massives, qu?il s?agisse de modèles de classification, d?optimisation
ou encore de visualisation, et d?autre part l?apprentissage profond,
l?apprentissage de représentations et les domaines d?applications
associés. Ces projets couvrent des thèmes relevant notamment des
équipes ACRO, BDA, QARMA et TALEP du LIF.
Au-delà de cette priorité thématique, toute candidature d?excellence
dans le périmètre du LIF est éligible.
Par ailleurs, la capacité du/de la candidat(e) à enrichir la dimension
technologique des recherches menées dans le cadre de ces projets et à
participer à des partenariats industriels est un plus incontestable.
Contact Recherche : Thierry Artières (thierry.artieres@centrale-marseille.fr)
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