http://www.dgdr.cnrs.fr/drhita/concoursita/consulter/resultats/consulter.htm

http://www.dgdr.cnrs.fr/drhita/concoursita/consulter/resultats/consulter.htm

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Proposition de travaux de these, IRISA, equipe Expression

Titre : Optimisation de scripts d'enregistrement pour la lecture expressive de livres audio

Mots-clefs : synthese de la parole expressive ; optimisation et apprentissage.

Contexte : Le projet vise a etudier la realisation automatique de livres audio a l'aide d'une

voix de synthese. La duree d'ecoute de l'integralite d'une uvre necessite une voix de haute

qualite a l'expressivite adaptee.

Un systeme de synthese vocale a partir du texte (TTS) produit un signal de parole

correspondant a une vocalisation d'un texte donne. Ces dernieres annees, la TTS a fait de

nombreux progres en termes de qualite acoustique et d'intelligibilite, mais la production

d'une voix expressive de tres bonne qualite reste un verrou scientique (voir [1] et ses

references). Cette qualite vocale depend fortement du systeme TTS (parametrique, ou par

selection et concatenation d'unites sonores) et du corpus de parole utilise.

Frequemment, la creation d'un tel corpus de parole necessite l'enregistrement de la lecture

d'un script specique avec des types d'expressivite donnes. Ce processus d'enregistrement

etant complexe et co^uteux, de nombreux travaux portent sur la creation d'un script

permettant de couvrir un maximun d'evenements souhaites tout en minimisant sa duree

(voir [2, 3, 4] et leurs references).

Proposition : La problematique etudiee dans ce projet de these est la creation de livres

audio sous une forme hybride : il s'agit d'enregistrer une partie minimale des livres vises

pour produire une voix de synthese la mieux adaptee au texte restant a vocaliser. Plus

generalement, le sujet porte sur l'etude des methodes de construction et d'enrichissement

automatique de scripts d'enregistrement an de produire une voix de synthese de haute

qualite pour des textes pre-denis d'expressivite variee. Cette approche se formalise en un

probleme d'optimisation d'un compromis entre qualite des messages acoustiques naux et

quantite de texte a enregistrer.

Un premier axe de travail concerne la problematique de l'evaluation subjective et objective.

Dans le cadre general de la synthese de la parole, l'evaluation de la qualite des signaux

produits est un probleme qui fait l'objet de nombreuses etudes (voir par exemple [5, 6, 7])

mais qui reste dicile. En quoi le fait de conna^tre a l'avance le texte a vocaliser ou de

disposer de signaux de parole naturelle realises dans le m^eme contexte permet de simplier

ce probleme ? D'un autre cote, le livre audio produit sera un melange de signaux naturels

et de signaux de synthese. Il sera donc necessaire d'etudier et de proposer des approches

speciques pour evaluer de tels objets et, en particulier, depasser l'evaluation subjective a

l'echelle de la phrase.

Un deuxieme axe de travail porte sur la construction automatique du script d'enregistrement

et la denition d'un compromis entre la qualite des signaux et la taille de l'enregistrement

associe. Plusieurs verrous sont deja identies. Comment les descripteurs textuels

inuencent-ils la qualite nale ? En particulier, quelles methodes d'apprentissage, guidees

par des mesures objectives de qualite, conduisent aux jeux de descripteurs optimaux ?

Un dernier axe de travail porte sur l'etude de la prise en compte des alterations entre le

resultat theorique attendu lie au script d'enregistrement et le signal acoustique reel issu de

la phase d'enregistrement. Comment detecter ces variations et adapter dynamiquement le

script an de conserver la qualite acoustique nale initialement attendue ?

Environnement de travail : le projet sera realise au sein de l'equipe Expression de l'IRISA,

dans sa composante lannionnaise specialisee sur les problematiques de synthese de la parole

et de traitement automatique des langues. Il sera encadre conjointement par Damien Lolive

1

et Jonathan Chevelu (IRISA-ENSSAT Lannion, Universite de Rennes1) et beneciera d'un

nancement sur trois ans (nancement des conseils departemental et regional). L'equipe

dispose d'un moteur de synthese de la parole par corpus, d'un moteur statistique (HTS),

d'un studio d'enregistrement, d'une plate-forme de tests d'ecoute [8] et d'une collection de

livres audio annotes [9] qu'elle enrichit dans le cadre d'un projet ANR.

Prol du candidat : Le candidat sera dipl^ome d'un master informatique ou de toute autre

formation equivalente. Compte-tenu du sujet, des competences avancees en algorithmique

et programmation seront requises. Le candidat disposera de la motivation et des facultes

necessaires pour aborder les domaines de recherche de la synthese de la parole, de l'apprentissage

articiel et du traitement automatique des langues.

Contacts :

Damien LOLIVE (damien.lolive@irisa.fr) et Jonathan CHEVELU (jonathan.chevelu@irisa.fr)

Bibliographie

[1] D. Govind, S. R. Mahadeva Prasanna, Expressive speech synthesis : a review, Int. J. of

Speech Tech., p. 1-24, 2013.

[2] H. Francois, Synthese de la parole par concatenation d'unites acoustiques : construction

et exploitation d'une base de parole continue, these de l'Univ. de Rennes 1, 2002

[3] D. Cadic, Optimisation du procede de creation de voix en synthese par selection, these

de l'Univ. de Paris 11, 2011

[4] N. Barbot, O. Boe
ard, J. Chevelu, A. Delhay, Large linguistic corpus reduction with

SCP algorithms, Computational Linguistics 41(3) : 355-383, 2015

[5] N. Campbell, Evaluation of speech synthesis : from reading machines to talking machines,

Evaluation of Text and Speech Synthesis, (L. Dybjoer at al. Eds.) , Chapitre 2,

2007

[6] J. Chevelu, D. Lolive, S. Le Maguer, D. Guennec, How to compare TTS systems : a

new subjective evaluation methodology focused on di
erences, Interspeech, 2015

[7] C.-T. Do, M. Evrard, A. Leman, C. d'Alessandro, A. Rilliard, J.-L. Crebouw, Objective

evaluation of HMM-based Speech synthesis system using Kullback-Liebler divergence,

Interspeech, 2015

[8] L. Blin, O. Boe
ard, V. Barreaud, WEB-based listening test system for speech synthesis

and speech conversion evaluation, LREC, 2008

[9] O. Boe
ard, L. Charonnat, S. Le Maguer, D. Lolive, Towards fully automatic annotation

of audio books for TTS, LREC, 2012

2

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Ingénieur(e) d’études – CDD 6 mois

Contexte et environnement

Dans le Projet MANES (Médiation et Accessibilité Numérique pour les Etudiants Sourds) financé par

l’UNR-RA), l’université Grenoble-Alpes recrute un ingénieur d’études pour un CDD de 6 mois (ou 2 CDD

de 3 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.

L’ingénieur(e) d’études recruté(e) participera à l’étape « expérimentation du dispositif » visant à tester

l’apport d’un dispositif de ce type pour favoriser la prise de notes des étudiants sourds.

Missions principales

L’ingénieur(e) d’études recruté(e) sera chargé(e), en lien avec les chercheurs participant au projet, de

l’élaboration, de la passation et de l’analyse des expérimentations auprès des étudiants sourds.

o Montage d’une pré-expérimentation vidéo en ligne (plateforme moodle)

o Quizz vidéo LSF en ligne à réaliser : élaboration des questions, élaboration des vidéos

questions en LSF, élaboration d’un module de questionnaire en ligne sur moodle

o Annotation des prises de notes (français écrit) et des restitutions (LSF)

o adaptation du système de sous-titrage au public sourd

Compétences

- Expérience de recherche souhaitée dans le domaine de la surdité et/ou dans le domaine des TICE.

- Compétences en LSF et en français écrit

- Compétences linguistiques pour l’analyse du français écrit et de la LSF

- Compétences multimédia : outil de transcription (ELAN), de sous-titrage, de montage vidéo et

création de modules en ligne (MOODLE ou autre plateforme pédagogique).

- Une expérience en programmation web serait un plus.

Profil du candidat

- Issu(e) d’un master dans le domaine des Sciences du Langage ; Industrie de la Langue, Informatique,

Sciences de l’éducation, psychologie

Contexte de travail

L’ingénieur d’études travaillera au sein du laboratoire LIDILEM (Laboratoire de LInguistique et de

Didactique des Langues Etrangères et Maternelles) ou LIG (Laboratoire d’Informatique de Grenoble).

Modalités de candidatures

Les candidatures (CV détaillé et lettre de motivation) sont à adresser au porteur du projet MANES :

Isabelle Estève (Isabelle.esteve@univ-grenoble-alpes.fr ) ; dès à présent et jusqu’au vendredi 24 juin.

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up to 4 PhD students in conversational systems and social robots

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href='http://ipvs.informatik.uni-stuttgart.de/mlr/research-group-leader/'>
Research Group Leader in Intelligent Human-Robot Cooperation
< /a>
at University Stuttgart, Germany. Email Marc Toussaint
< marc.toussaint@informatik.uni-stuttgart.de>

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Title: 1 PhD position in bio-inspired ASR, 1 PhD position in brain-signal based ASR

The Center for Translational Neurophysiology of Speech and Communication (CTNSC) at the Italian Institute of Technology is seeking for motivated PhD students who will work on bio-inspired automatic speech recognition (ASR) and brain-signal based ASR.

The successful candidates will have a Master degree in Computer Science, Engineering (or equivalent) and a background in machine learning and/or signal processing.

The candidates should apply by the 5^th of August, 2016.

PhD courses will start on the 1^st of November, 2016.

CTNSC Info: https://www.iit.it/centers/ctnsc-unife

PhD page [ITA]:

http://www.unife.it/studenti/dottorato/concorsi/ordinario

PhD course summary [ITA]:

http://www.unife.it/studenti/dottorato/modulistica/concorsi/32-ciclo/neuroscienze

How to participate to the selection [ITA/ENG]:

http://www.unife.it/studenti/dottorato/modulistica/Guidaconcorso.pdf

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Job offer: permanent research position in machine learning for natural language processing

Mission: Conceive algorithms and develop softwares for natural language processing, with applications to automatic extraction of structured knowledge from documents

Position environment: In Orange Labs,  the « CONTENT » department prepares the future of content access services (music, video, press, radio, education, entertainment). The job is opened in a multidisciplinary team, located in Lannion, France, in which are conducted collaborative studies with academics (collaborative projects, PhD supervision, scientific publications) and research and development projects with strong interactions with operational units of Orange (marketing, customer experience, design?)  

Candidate profile: PhD or significant research experience in the field of machine learning, with an noticeable interest on natural language processing

In order to visualize the full offer and to apply on line : https://orange.jobs/jobs/offer.do?joid=54926&lang=FR

For more information, please contact:

Gilles Prigent (gilles.prigent@orange.com)

Delphine Charlet (delphine.charlet@orange.com)

Géraldine Damnati (geraldine.damnati@orange.com)

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ASR Research Scientist (Chinese) Oben Pasadena, California

  
Founded in 2014, ObEN is an artificial iintelligence company based in one of the world’s most successful incubators: Idealab in Pasadena, CA. We have developed a sophisticated speech technology that allows any Internet of Things to speak in any voice and in any language. 

 
JOB DESCRIPTION  As a Speech Recognition Research Scientist at ObEN, you’ll be working on several proprietary and stealthy projects in the audio space. Your work will encompass the whole scope of application development, including speech research, voice interface design, application prototyping, and development of our proprietary speech recognition engine. Currently, we are mainly interested in candidates with experience in ASR for Chinese language. Experience in English and/or Spanish is a big plus.  
You must have:

■ PhD in Computer Science, Electrical Engineering or Mathematics with specialization in speech recognition, natural language processing or machine learning.

■ Solid experience building ASR systems (Chinese) and/or a publication record in the area.

■ Solid experience with Kaldi.

■ Strong machine learning background and familiar with standard statistical modeling techniques applied to speech.

■ Familiarity with linguistic phonetics.

■ Proficiency in programming languages such as C/C++, Python, Java or Perl.

■ Knowledge of basic digital signal processing techniques for audio.

■ Enjoys a highly collaborative environment. 

To apply please send the following: to careers@oben.me

● Detailed resume and/or Linkedin profile.

● Links to prominent scientific/professional contributions.

Contact: Fernando Villavicencio <fernando@oben.com>

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In the context of a Swiss NSF grant, we seek a PhD student to work on multilingual and
affective speech synthesis.

  http://www.idiap.ch/education-and-jobs/job-10193

The research will begin with the state of the art in speech synthesis, probably based
around deep neural networks (DNNs). Such networks have been shown recently to produce
synthetic speech with better overall quality, especially with respect prosody, than other
techniques. The student will then focus on modelling of speech with a view to extraction,
modeling and adaptation of emotion (affect) in synthetic speech. To demonstrate the
language independence of the models, it will be necessary to work in two or three
languages.

Several research threads are possible; one thread will involve creating a neural model of
prosodic features. It will build upon recent work here at Idiap where we have created a
general physiologically plausible model for prosody.

Another research thread will involve analysis and modification of the phonetic quality of
the synthetic speech. This is in keeping with observations that formant positions can
change with affective indicators such as valence and arousal.

Depending on progress, there is also the possibility to address cross-lingual modelling
of affect. Here, the goal would be to detect emotion in one language and reproduce it in
another language.

Owing to availability of data, the work will probably proceed using German data. However,
our locality dictates that evaluation will be easier in French. Amongst the Swiss
languages, Italian is also appealing.

The ideal Ph.D student should have a master (or equivalent) degree in engineering,
computer science, or applied mathematics. S/he should have a good background in
mathematics, statistics, and programming (C/C++, Python, scripting langages). Given the
multlingual nature of the project, the position would suit someone with some knowledge of
one or more of the languages described above. In order to balance the group, we
especially encourage female applicants. However, all applications will be judged on merit.

Applications will be considered as they are received; the position will remain open until
filled.

About Idiap:

Idiap is an independent, non-profit research institute recognized and supported by the
Swiss Government, and affiliated with the Ecole Polytechnique Fédérale de Lausanne
(EPFL). It 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 exciting
recreational activities, including hiking, climbing and skiing, as well as varied
cultural activities. It is within close proximity to Geneva and Lausanne. Although Idiap
is located in the French part of Switzerland, English is the working language. Free
French lessons are provided.

Idiap offers competitive salaries and conditions at all levels in a young, dynamic, and
multicultural environment. Idiap is an equal opportunity employer and is actively
involved in the 'Advancement of Women in Science' European initiative. The Institute
seeks to maintain a principle of open competition (on the basis of merit) to appoint the
best candidate, provides equal opportunity for all candidates, and equally encourage both
genders to apply.

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

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Speech Analytics Engineer

Job Description

SRI’s Speech Technology and Research (STAR) Lab seeks a self-motivated and team-oriented speech engineer to join a team working on modeling of speaker state (for example, emotion and health). The position offers both research and commercial opportunities, and a potential for growth and client interaction. The work will include advancing technology capabilities, understanding client data and conducting experiments involving model development and testing, under current or new projects in this area. This is an opportunity to have a significant impact in an emerging research area.

STAR Lab engages in leading-edge research in speech recognition, speaker characterization, speaker and language identification, machine translation, natural language processing and other areas of speech/language technology, offering opportunities from basic research to prototyping, productization, and widespread deployment. The multidisciplinary research team consists of excellent speech researchers, linguists and software engineers. Characteristics of STAR staff are enthusiasm, self-motivation, initiative, passion for learning, taking ownership, thriving in a flat organizational hierarchy, and a desire to control their own career paths.

SRI International is a premier nonprofit research organization based in Menlo Park, California with a mission to create world-changing solutions making people safer, healthier, and more productive.

Located in the heart of Silicon Valley, the world’s center for innovation and technology, SRI’s focus spans everything from basic research to delivered systems, government and commercial work and spinning off successful companies like SIRI. Flexible cross-laboratory teams often form dynamically to solve challenging problems spanning multiple disciplines.

Requirements

-PhD (preferred) or Master’s degree in electrical engineering or computer science, with experience in advanced speech signal processing and in state of the art machine learning techniques for speech processing.

-Additional background in natural language processing is useful.

-Work experience outside the PhD institution, including internships, is preferred.

-Candidates should have an interest in the topic area, excellent programming skills, be a quick learner, be proactive and efficient, and work well both individually and in teams.

Apply online at www.sri.com/careers

Job ID: 1239BR

SRI is an Equal Employment Opportunity/Affirmative Action Employer

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Announcing 14 PhD positions on a new Marie Curie European Training Network, ENRICH (Enriched Communication Across the Lifespan)

Speech is a hugely efficient means of communication: a reduced capacity in listening or speaking creates a significant barrier to social inclusion at all points through the lifespan, in education, work and at home. Hearing aids and speech synthesis can help address this reduced capacity but their use imposes greater listener effort. The fundamental objective of ENRICH is to modify or augment speech with additional information to make it easier to process. Enrichment aims to reduce the listening burden by minimising cognitive load, while maintaining or improving intelligibility. ENRICH will investigate the relationship between cognitive effort and different forms of natural and synthetic speech.

ENRICH is funded for 4 years from October 2016. We are now looking to recruit 14 generously-funded early stage researchers to undertake projects leading to a PhD within the network. Openings exist for candidates with a background in engineering, psychology, speech science or linguistics.

ENRICH partners are: University of Edinburgh, Radboud University, Tobii Technology, University College London, University of Crete, Hoerzentrum Oldenburg, University Medical Center Groningen and the University of the Basque Country (coordinator).

For more details of the network and early stage researcher positions, see http://www.enrich-etn.eu. The closing date for applications is 30 September 2016.

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Le service audio de la Police Technique et Scientifique (Ecully, près de Lyon) recherche des vacataires pour effectuer un travail de segmentation et/ou d'enregistrements de locuteurs. 
Le profil suivant est recherché:

ou de téléphoner au 0472868638

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Nous offrons un contrat doctoral de 3 ans portant sur la « Caractérisation et la génération de l'expressivité en fonction des styles de parole pour la construction de livres audio » dans le cadre du projet ANR SynPaFlex. Des informations sur ce travail sont disponibles à : https://www-expression.irisa.fr/files/2016/08/phd_caracterisation_generation_livre_audio.pdf et https://www-expression.irisa.fr/files/2016/08/phd_audiobook_generation.pdf pour la version en anglais. Cette thèse sera effectuée en co-direction entre l?IRISA et le LLF. 

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Looking for an opportunity in linguistics / phonetics 

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We are asking for input on researchers’ interest and engagement in computational approaches to linguistic Code-Switching in any language pair, modality, or genre. A brief survey can be found at the link below.

https://docs.google.com/forms/u/0/d/1ARm04N_si_7VaMPjtbWOFUUjxJZm7TQmjgNSHcUNPcw

Mona Diab

Julia Hirschberg

Thamar Solorio

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Applications are invited for Research Scientist positions at Nokia Bell Labs, Cambridge in the areas of Applied Machine Learning, Embedded Systems and Sensor engineering with a strong focus on Pervasive Sensing and Mobile Systems.

Nokia and Bell Labs

Nokia is a global leader in the technologies that connect people and things. Powered by the pioneering work of Bell Labs, our research and innovation division, and Nokia Technologies, we are at the forefront of creating and licensing the technologies that are increasingly at the heart of our connected lives. Nokia Bell Labs is internationally renowned as the birthplace of modern information theory, the transistor, the laser and the UNIX operating system.

Bell Labs Cambridge

Bell Labs' research facility in Cambridge is a leading lab working in the areas of Mobile Sensing and Systems, Applied Machine Learning, Social Computing and Internet of Things research.

We have openings for inspired innovators in our Pervasive Sensing and Systems Department of  the Application Platforms and Software Systems (A&S)  research Program. The department's research agendas are:

• Embedded OS-level software enabling resource-efficient high-precision sensor processing, modelling and analytics.
• Multi-modal edge-device-based deep learning models of human behaviour and context reasoning in the wild.
• Exploration and understanding of new sensor modalities.
• Disruptive end-to-end applications in the areas of digital health, smart home and quantified enterprise.

Main duties and responsibilities

• Carry out groundbreaking research in the areas of Mobile Systems, Ubiquitous Computing and Applied Machine learning with focus on above mentioned research agendas for creating both theoretical innovations and novel practical implementations.
• Contribute to the technical definition of research objectives and programs.
• Propose and publish research for the major research publications worldwide.
• Create and maintain strong collaborative associations with university-based researchers, other leading research bodies, and product business units.
• Keep an active and visible role in the research community through conference committees and reviewing panels.

Expected qualifications, skills and experience required for this job

A PhD in Computer Science or Electrical Engineering with a strong focus on UbiComp and Mobile Computing. A PostDoc experience is a plus.

• For positions with a focus on Embedded Systems
• Deep understanding of Mobile and Embedded Operating Systems with kernel level coding experience.
• Comprehensive understanding of Mobile and Embedded Operating Systems and Mobile Application Frameworks (e.g., Android / iOS).

• For positions with focus on (Applied) Machine Learning
• Deep expertise in Machine Learning and Data Mining techniques (especially deep learning methods) for processing sensory signals (vision, speech, motion, etc.)
• Good understanding of Mobile and Embedded Operating Systems and Mobile Application Frameworks (e.g., Android / iOS).

• For positions with a focus on Sensor Engineering
• Hands-on architecture-level skills in hardware integration and sensor engineering
• Strong expertise in the areas of digital signal processing and applied machine learning

• A proven track record in research, with publications in prestigious journals and conferences.
• Strong written and spoken communications skills.
• Ability to conduct independent research while also contributing to a team-oriented project.

For informal query or more information please contact Dr. Fahim Kawsar (fahim.kawsar@nokia-bell-labs.com) or Dr. Nic Lane (nic.lane@nokia-bell-labs.com).

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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 15^th 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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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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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

• MSc/PhD in Computer Science or equivalent with experience in Machine Learning (including DNNs), Artificial Intelligence or Statistics.

• Creative thinking with the ability to drive their ideas into technology.

• NLP background or experience.

• Proactivity, initiative, positive attitude, ability to solve problems creatively.

• Excellent speaking and writing skills in English.

Nice to have

• Enterprise experience in this area.

• Experience with both supervised and unsupervised learning.

• Data warehousing and ETL.

• Knowledge of ML platforms such as Azure ML, Google Tensor, IBM Watson.

• Experience in statistical analysis and respective tools (e.g. SPSS).

• 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:

• BA or Masters in Linguistics or equivalent. Masters in Computational Linguistics is a plus.

• Experience in project management: gathering requirements; planning; resource allocation; communication with developers.

• Technical Skills: Python; Perl; Regular Expressions; SQL; Excel; Microsoft Office Suite;

• Proficiency in more than 2 languages.

• Proactivity, initiative, positive attitude, ability to solve problems creatively.

• Excellent speaking and writing skills in English.

• Good communication skills and ability to work with ambiguity.

• 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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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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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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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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