ISCA - International Speech
Communication Association


ISCApad Archive  »  2020  »  ISCApad #259  »  Jobs

ISCApad #259

Friday, January 10, 2020 by Chris Wellekens

6 Jobs
6-1(2019-07-11) 3year Early Stage Researcher PhD positions

Applications are invited for a three-year Early Stage Researcher PhD positions in the

speech technology for pathological speech.

Description

The thesis focuses on studying the link between the internal representations of Deep Neural Networks (DNNs) and

the subjective representation of speech intelligibility. We propose to explore the saliency detection capabilities of

DNNs when used in a regression task for predicting speech intelligibility scores as given by human experts. By

saliency, we mean to retrieve which frequency bands are important and used by a DNN to make its predictions.

The final expectation is to identify regions of interest in the speech signal, both in time and frequency, that

characterise the level of speech impairment.

The experiments will be processed on various samples of speech performed by 150 people (100 patients and 50

healthy controls). This database was recorded within the INCA C2SI project, and contains speech from patients

treated for cancer of the oral cavity or pharynx. It contains also various metadata such as the location of the tumor,

the impairment in terms of severity and intelligibility that were appreciated by human experts, self evaluation

questionnaires on the patient’s quality of life… Various tasks were recorded such as a sustained vowel, read

speech, nonsense words, prosodic exercises, picture description, etc. There will be also the possibility to extend

the work to another corpus which is composed of voice of patients suffering from Parkinson disease.

At first, the PhD will have to take benefit from the various analysis and descriptions that were done during the C2SI

project trying to correlate the impact of the tumor and the communication ability. Those results will help attesting

the human representation of the impact of the disease. Then, a DNN representation will be modeled to fit the data,

taking care of the data sparsity. The last part of the work will be to explore the intern representation of the DNN,

trying to explore what part of the signal help to make a decision on the impact of the disease and that will be the

final goal of the thesis, studying the automatic representation that lies in the model the student will propose.

This work is funded by the TAPAS project (https://www.tapas-etn-eu.org) which is a Horizon 2020 Marie

Skłodowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project that aims

to transform the well being of people across Europe with debilitating speech pathologies (e.g., due to stroke,

Parkinson's, etc.). These groups face communication problems that can lead to social exclusion. They are now

being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but

which is not robust to atypical speech.

The supervision of the PhD will take place at IRIT laboratory by the SAMoVA team in Toulouse. SAMoVA does

research in the domain of “analysis, modeling and structuring of audiovisual content”. The application areas are

diverse: speech processing, identification of languages, speaker verification and speech and music indexing. The

researchers expertise covers novel machine learning and audio processing technologies and is now focused on

deep learning methods, leading to several publications in international conferences.

Eligibility Criteria

Early Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four

years (full-time equivalent research experience) of their research careers.

- The ESR may be a national of a Member State, of an Associated Country or of any Third Country.

- The ESR must not have resided or carried out her/his main activity (work, studies, etc.) in the country of her/his

host organization for more than 12 months in the 3 years immediately prior to her/his recruitment.

- Holds a Master’s degree or equivalent, which formally entitles to embark on a Doctorate.

- Does not hold a PhD degree.

Duration of recruitement: 36 months

Contact: Julie Mauclair (mauclair@irit.fr)

 

 

Top

6-2(2019-07-17) Chief Technical Officer (CTO) at ELDA

Chief Technical Officer (CTO)

Under the supervision of the CEO, the responsibilities of the Chief Technical Officer (CTO) include planning and supervising technical development of tools, software components or applications for language resource production and management.
He/she will be in charge of managing the current language resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded projects.

This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.

Profile:
?    PhD in Computer Science, Natural Language Processing, or equivalent
?    Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.)
?    Familiarity with open source and free software
?    Knowledge of a statically typed functional programming language (OCaml preferred) is a plus
?    Good level in English, with strong writing and documentation skills in English
?    Dynamic and communicative, flexible to work on different tasks in parallel
?    Ability to work independently and as part of a multidisciplinary team
?    Citizenship (or residency papers) of a European Union country
?    Good level in Python, knowledge of Django would be a plus
?    Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)

Salary: Commensurate with qualifications and experience (between 45-55K?).
Other benefits: complementary health insurance and meal vouchers

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org

ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

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

Top

6-3(2019-07-19) Two Post-doctoral positions at Le Mans University , France

 2 Post-doctoral positions at Le Mans University on Deep learning approaches speech processing

*Place of work* Le Mans University, Le Mans ? France

*Starting date* From now to June 2020

*Salary* between 2 300 and 2 600 ? /month

*Duration* 12 months and 24 months (can be combined in a 36 months position)

****************************************
1st position
****************************************

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

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

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

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

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

* Start * as soon as possible, latest January 2020

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

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

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

****************************************
2nd position
****************************************

* Context *
The LST team from LIUM (Le Mans University) is focusing on evolutive end-to-end
neural networks for speaker recognition. The Extensor project (French ANR funded)
aims at developing novel architectures for end-to-end speaker recognition as well as
explaining the behavior of those networks. The focus of Extensor is threefold:
get rid of the legacy of bayesian system?s architecture and explore wider opportunities offered in deep learning;
explore real end-to-end architectures exploiting the tax signal instead of classical features (such as MFCC of filterbanks);
Develop tools for explainability in speaker recognition.

* Missions *
Develop end-to-end speaker recognition system based on state-of-the-art approaches  (x-vectors, sincnet?)
Develop evolutive architectures making use of existing genetic algorithms and study their behavior.
Participate to the three hackathons organized by the Extensor project in order to develop
tools for evolutive neural network architecture and explainability for speaker recognition.
Dissemination: the research will be published in the major conferences and journals

* Duration * 24 months
* Salary * 2 600? (after taxes)

* Start * as soon as possible, latest June 2020

* Location * LIUM, Le Mans University

* Superviser * Anthony Larcher (anthony.larcher@univ-lemans.fr)

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

 

--

Anthony Larcher Maître de Conférences, HDR / Associate Professor
Directeur de l'Institut Informatique Claude Chappe
co-responsable de la Spécialité Informatique
Responsable de l'option Interface Personnes Systèmes Tél. +33 (0)2 43 83 38 30
Avenue Olivier Messiaen, 72085 - LE MANS Cedex 09 univ-lemans.fr

Top

6-4(2019-07-20) Three-year Early Stage Researcher PhD positions, IRIT, Toulouse, France

Applications are invited for a three-year Early Stage Researcher PhD positions in the

speech technology for pathological speech.

Description

The thesis focuses on studying the link between the internal representations of Deep Neural Networks (DNNs) and

the subjective representation of speech intelligibility. We propose to explore the saliency detection capabilities of

DNNs when used in a regression task for predicting speech intelligibility scores as given by human experts. By

saliency, we mean to retrieve which frequency bands are important and used by a DNN to make its predictions.

The final expectation is to identify regions of interest in the speech signal, both in time and frequency, that

characterise the level of speech impairment.

The experiments will be processed on various samples of speech performed by 150 people (100 patients and 50

healthy controls). This database was recorded within the INCA C2SI project, and contains speech from patients

treated for cancer of the oral cavity or pharynx. It contains also various metadata such as the location of the tumor,

the impairment in terms of severity and intelligibility that were appreciated by human experts, self evaluation

questionnaires on the patient’s quality of life… Various tasks were recorded such as a sustained vowel, read

speech, nonsense words, prosodic exercises, picture description, etc. There will be also the possibility to extend

the work to another corpus which is composed of voice of patients suffering from Parkinson disease.

At first, the PhD will have to take benefit from the various analysis and descriptions that were done during the C2SI

project trying to correlate the impact of the tumor and the communication ability. Those results will help attesting

the human representation of the impact of the disease. Then, a DNN representation will be modeled to fit the data,

taking care of the data sparsity. The last part of the work will be to explore the intern representation of the DNN,

trying to explore what part of the signal help to make a decision on the impact of the disease and that will be the

final goal of the thesis, studying the automatic representation that lies in the model the student will propose.

This work is funded by the TAPAS project (https://www.tapas-etn-eu.org) which is a Horizon 2020 Marie

Skłodowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project that aims

to transform the well being of people across Europe with debilitating speech pathologies (e.g., due to stroke,

Parkinson's, etc.). These groups face communication problems that can lead to social exclusion. They are now

being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but

which is not robust to atypical speech.

The supervision of the PhD will take place at IRIT laboratory by the SAMoVA team in Toulouse. SAMoVA does

research in the domain of “analysis, modeling and structuring of audiovisual content”. The application areas are

diverse: speech processing, identification of languages, speaker verification and speech and music indexing. The

researchers expertise covers novel machine learning and audio processing technologies and is now focused on

deep learning methods, leading to several publications in international conferences.

Eligibility Criteria

Early Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four

years (full-time equivalent research experience) of their research careers.

- The ESR may be a national of a Member State, of an Associated Country or of any Third Country.

- The ESR must not have resided or carried out her/his main activity (work, studies, etc.) in the country of her/his

host organization for more than 12 months in the 3 years immediately prior to her/his recruitment.

- Holds a Master’s degree or equivalent, which formally entitles to embark on a Doctorate.

- Does not hold a PhD degree.

Duration of recruitment: 36 months.

Contact : Julie Mauclair (mauclair@irit.fr)

Top

6-5(2019-07-23) PhD position at LORIA-INRIA, Nancy, France
Automatic classification using deep learning of hate speech posted on the Internet


Supervisors: Irina Illina, MdC, HDR, Dominique Fohr, CR CNRS
Team: Multispeech, LORIA-INRIA, France
Contact: illina@loria.fr, dominique.fohr@loria.fr
Duration of PhD Thesis : 3 years
Deadline to apply : August  15th, 2019
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

Keywords: hate speech, social media, natural language processing.

The rapid development of the Internet and social networks has brought great benefits to women and men in their daily lives. Unfortunately, the dark side of these benefits has led to an increase in hate speech and terrorism as the most common and powerful threats on a global scale. Hate speech is a type of offensive communication mechanism that expresses an ideology of hatred often using stereotypes. Hate speech can target different societal characteristics such as gender, religion, race, disability, etc. Hate speech is the subject of different national and international legal frameworks. Hate speech is a type of terrorism and often follows a terrorist incident or event.


Social networks are incredibly popular today. Nowadays, Twitter, LinkedIn, Facebook and YouTube are used as a standard tool for communicating ideas, beliefs and feelings. Only a small percentage of people use part of the network for unhealthy activities such as hate speech and terrorism. But the impact of this low percentage of users is extremely damaging. For years, social media companies such as Twitter, Facebook and YouTube have invested hundreds of millions of dollars each year in the task of detecting, classifying and moderating hate. But these efforts are mainly based on manually revising the content to identify and remove offensive content, which is extremely expensive.

This thesis aims at designing automatic and evolving methods for the classification of hate speech in the field of social media. Despite the studies already published on this subject, the results show that the task remains very difficult. We will use semantic content analysis methodologies from automatic language processing (NLP) and methodologies based on deep learning (DNN) which is the revolution in the field of artificial intelligence. During this thesis, we will develop a research protocol to classify hate speech in the text in terms of hateful, aggressive, insulting, ironic, neutral, etc. character. This type of problem is placed in the context of the multi-label classification.

In addition, the problem of obfuscation of words in hate messages will need to be addressed. People who want to write hate speech on the Internet know that they risk being censored by rudimentary automatic systems of moderation. So, users try to obscure their words by changing the spelling or the spelling of words.

Among the crucial points of this thesis are the choice of the DNN architecture and the relevant representation of the data, ie the text of the internet message. The system designed will be validated on real flows of social networks.

Skills

Strong background in mathematics, machine learning (DNN), statistics

Following profiles are welcome, either: Strong experience with natural language processing

Excellent English writing and speaking skills are required in any case.

References :
T Gröndahl, L Pajola, M Juuti, M Conti, N Asokan (2018) ?All You Need is? Love?: Evading Hate-speech Detection, arXiv preprint arXiv:1808.09115
Wiegand, M., Klakow, D. (2008). Optimizing Language Models for Polarity Classification. In Proceedings of ECIR, pp. 612-616.
Wiegand, M., Ruppenhofer, J. (2015). Opinion Holder and Target Extraction based on the Induction of Verbal Categories. In Proceedings of CoNLL, pp. 215-225.
Wiegand, M., Ruppenhofer J., Schmidt A.,  C. Greenberg (2018) Inducing a Lexicon of Abusive Words ? A Feature-Based Approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Wiegand, M., Wolf, M., Ruppenhofer, J. (2017) Negation Modeling for German Polarity Classification. In Proceedings of GSCL.
Zhang Z., Luo L. (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
Top

6-6(2019-07-29) PhD position, Vrij Universiteit Berussels, Belgkium

PhD position in

Agent Based Modeling of

Cognitively Plausible Emergent Behavior

 

In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a PhD student for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.

 

PhD position offered

We offer a four year PhD position funded by a scholarship with a yearly bench fee. The PhD work will consist of building an agent-based simulation in which we can investigate emergence of behavior in a cognitively realistic setting. This means that the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. The modeling will primarily focus on emergence of speech, but the simulation should be general enough that it can be easily adapted to other areas, such as traffic or economic interactions.

 

What we are looking for

We are looking for an enthusiastic student with a degree in artificial intelligence, cognitive science, linguistics or equivalent and who has experience programming agent-based or cognitive models, preferably in Python or C++. Knowledge of speech and speech processing is a bonus. The starting date is negotiable, but preferably no later than September 2019.

 

How to apply

Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.

 

Links

Context: https://ai.vub.ac.be/node/1687

Email Bart de Boer: bart@ai.vub.ac.be

Top

6-7(2019-07-29) Visiting postdoc at Vrije Universiteit, Brussels, Belgium

Visiting postdoc in

Cognitively Plausible Emergent Behavior

 

In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a short term (three-six months) visiting postdoc for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.

 

Position offered

We offer a three-six months visiting postdoc position funded by a scholarship and with a bench fee. The work should consist of agent-based simulation, or of experiments to investigate emergence of behavior in a cognitively realistic setting. This means that in a computer simulation, the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. Experiments should focus on factors that are typical for human settings, but that are generally idealized away, such as altruism, conflicts of interests and other 'non-rational' behaviors. We are most interested in modeling emergence of speech, but we welcome applications proposing other areas, such as traffic or economic interactions.

 

What we are looking for

We are looking for an enthusiastic postdoc with a track record in artificial intelligence, cognitive science, linguistics or equivalent and who has either experience programming agent-based or cognitive models, or who has experience with the interaction between computer models and experiments. The starting date is negotiable, but preferably no later than September 2019.

 

How to apply

Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. Be sure to include a short (1-page) outline of your proposed project in the letter of motivation, as well as a short planning. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.

 

Links

Context: https://ai.vub.ac.be/node/1687

Email Bart de Boer: bart@ai.vub.ac.be

 

 

Top

6-8(2019-08-02) Research engineer or Post-doc, at Eurecom, Inria, LIA, France

EURECOM (Nice, France), Inria (Nancy, France) and LIA (Avignon, France) are opening a
18-month Research Engineer or Postdoc position on speaker de-identification and voice
privacy.

For more information and to apply:
https://jobs.inria.fr/public/classic/en/offres/2019-01937

Top

6-9(2019-08-02) Ph.D. position in Softbank robotics and Telecom-Paris, France

Ph.D. position in Softbank robotics and Telecom-Paris
 
Subject:  Automatic multimodal recognition of users? social behaviors
in human-robot interactions (HRI)

*Places of work*  Softbank Robotics [SB] (Paris 15e) & Telecom Paris [TP] Palaiseau (Paris outskirt)

*Starting date* December 2019

*Funding* CIFRE http://www.anrt.asso.fr/fr/cifre-7843

*Context*
The research activity of the Ph.D. candidate will contribute to :
- Softbank Robotics robot?s software NAOqi, within the Expressivity team responsible for ensuring an expressive, natural and fun interaction with our robots.
- the Social Computing topic [SocComp.] of the S2a team [SSA] at Telecom-ParisTech, in close collaboration with other researchers and Ph.D. students of the team.

* Candidate profile*
As a minimum requirement, the successful candidate should have:
?    A master in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, cognitive sciences, affective computing, reinforcement learning, natural language processing, speech processing
?    Excellent programming skills (preferably in Python)
?    Excellent command of English
?    Very good communication skills, commitment, independent working style as well as initiative and team spirit

Given the multidisciplinary aspect of the subject, priority will be given to multidisciplinary profiles. Ph.D. applicant?s interest in social robotics is required.

*Keywords* Human-Machine Interaction, Social Robotics, Deep Learning, Social Computing, Natural Language Processing, Speech Processing, Computer Vision, Multimodality

*Supervision* :  
Industrial: Marine Chamoux (Softbank robotics),
Academic: Chloé Clavel [Clavel],  Giovanna Varni [Varni] (Telecom-Paris)

*How to apply*
Applications should be sent as soon as possible (the first review of applications will be made in early September). The application should be formatted as **a single pdf file** and should include:
?    A complete and detailed curriculum vitae
?    A letter of motivation
?   The academic credentials and the transcript of grades
?    The contact of two referees

The pdf file should be sent to the three supervisors: mchamoux@softbankrobotics.com, chloe.clavel@telecom-paristech.fr, giovanna.varni@telecom-paristech.fr



*Description*
Social robotics, and more broadly human-agent interaction is a field of human-machine interaction for which the integration of social behaviors is expected to have great potential. 'Socio-emotional behaviors' (emotions, social stances) include thus the role and the reactions of the user towards the robot during an interaction. These behaviors could be expressed differently depending:
-on the user (age, emotional state, ...): some users may have a dominant behavior with the robot, considering it a tool to achieve a goal. Others are more cooperative with the robot, they can be more friendly with it. Still others try to trap or 'troll' the robot.
-on the interaction context  (users do not behave in the same way when interacting with a pepper selling toys, or with a pepper bank secretary). Besides, in each of these situations, the robot must be able to adapt its behavior, and to provide a coherent interaction between the user and the robot, avoiding confusion and frustration.

This Ph.D. will focus on multimodal modeling for the prediction of the user's socio-emotional behaviors during interactions with a robot and on building an engine that is robust to real-life scenarios and different contexts. In particular, the Ph.D. candidate will address the following points:
- the encoding of contextual multimodal representations relevant for the modeling of socio-emotional behavior. Thanks to the robot, we have access to a lot of information on context (market, robot intention, demographics, multi or mono user interaction, etc.) that could be combined to our multimodal representation.
- the development and evaluation of models that take advantage of the complementarity of modalities in order to monitor the evolution of the user's socio-emotional behaviors during the interaction (e. g. taking into account the inherent sequentially of the interaction structure)
The models will be based on sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou] and [BenYoussef19].

Selected references of the team:
[Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews.  in AAAI 2019
[Garcia] Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d?Alche-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, ICML 2018
[Clavel&Callejas] Clavel, C.; Callejas, Z., Sentiment analysis: from opinion mining to human-agent interaction, Affective Computing, IEEE Transactions on, 7.1 (2016) 74-93.
[Langlet] C. Langlet and C. Clavel, Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user?s likes and dislikes , in ACL 2015
[Maslowski]  Irina Maslowski, Delphine Lagarde, and Chloé Clavel.  In-the-wild chatbot corpus: from opinion analysis to interaction problem detection, ICNLSSP 2017.
[Ben-Youssef17]  Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim.  Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions.  In  Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464?472. ACM, 2017.
[Ben-Youssef19] Atef Ben Youssef; Chloe Clavel; Slim Essid Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction, IEEE Transactions on Affective Computing, 2019
[Varni] Varni G., Hupont, I., Clavel, C., Chetouani, M. Computational Study of Primitive Emotional Contagion in Dyadic Interactions. IEEE Transactions on Affective Computing, 2017.

[SB] https://www.softbankrobotics.com/emea/fr
[TP] https://www.telecom-paristech.fr/eng/  
[SocComp.] https://www.tsi.telecom-paristech.fr/recherche/themes-de-recherche/analyse-automatique-des-donnees-sociales-social-computing/
[SSA] http://www.tsi.telecom-paristech.fr/ssa/#
[Clavel] https://clavel.wp.imt.fr/publications/
[Varni] https://sites.google.com/site/gvarnisite/



Top

6-10(2019-08-03) Speech scientist at ETS Research

Speech scientist at ETS Research :

 

https://etscareers.pereless.com/index.cfm?fuseaction=83080.viewjobdetail&CID=83080&JID=290092

Top

6-11(2019-08-12) Several positions in Forensic Speech Science or Forensic Data Science: Aston University, Birmingham, UK

Positions in Forensic Speech Science or Forensic Data Science:

- One Lecturer or Senior Lecturer

- Two Postdoctoral Researchers

 

Aston University, Birmingham, UK

 


Aston University has recently been awarded GBP 5.4 M from Research England?s Expanding Excellence in England (E3) Fund. The money is being used to expand the existing Centre for Forensic Linguistics into the substantially larger Aston Institute for Forensic Linguistics (AIFL). As part of the expansion, we are building a research team with expertise in forensic speech science and in forensic data science. In addition to conducting research in forensic speech science, members of the team will work on forensic inference and statistics more broadly, and on quantitative-measurement and statistical-model based approaches in other branches of forensic science. The latter potentially include but are not limited to: fingerprints, face, gait, ballistics, blood pattern analysis, and linguistics. The Forensic Speech Science Laboratory and the Centre for Forensic Data Science will be headed by Dr Geoffrey Stewart Morrison, and, in addition to the affiliation with AIFL, will be affiliated with the Computer Science Department in the School of Engineering and Applied Science.

 

We are looking to recruit the following positions:

 

Lecturer or Senior Lecturer in Forensic Speech Science or Forensic Data Science

Reference:    R190354


Salary:    Grade 9 £40,792 ? £48,677 or Grade 10 £50,132 ? £58,089

Contract Type:    Continuing 

Basis:    Full time

Closing Date:    23.59 hours BST on September 30, 2019

Interview Date:    To be confirmed

 

Two Postdoctoral Researchers in Forensic Speech Science or Forensic Data Science

Reference:    R190353


Salary:   Grade 8 £33,199 ? £39,609 or Grade 9 £40,792 ? £48,677

Contract Type:    Fixed term (3 years) 

Basis:    Full time or part time

Closing Date:    23.59 hours BST on September 30, 2019

Interview Date:    To be confirmed

 

The Lecturer or Senior Lecturer position will be a full-time permanent position and will include teaching and administrative responsibilities. The position is costed as a Grade 9 Lecturer, but an exceptionally well qualified and experienced successful applicant could potentially be appointed as a Grade 10 Senior Lecturer. Note: ?Lecturer? is equivalent to North American ?Assistant Professor?, ?Senior Lecturer? is equivalent to North American ?Associate Professor?, and ?Reader / Associate Professor? is an occasionally used additional rank between Senior Lecturer and Professor.

 

The Postdoctoral Researcher positions may be filled as full-time appointments (preferred) or via a combination of part-time appointments. The Postdoctoral Researcher positions will be fixed-term, but the plan is to build a team that will be successful in obtaining additional research funding that will sustain these positions.

 

All new team members must have a commitment to solving forensic problems. Previous experience working on forensic problems would be advantageous, but not essential. A background in forensic speech science, in other branches of forensic science, and/or in forensic inference and statistics would be advantageous, but not essential. At least one of the new team members must have a strong background in state-of-the-art automatic speaker recognition, with an ability to implement systems. Other useful backgrounds for members of the team would include biometrics, machine learning, natural language processing, and acoustic phonetics.

 

Candidates may apply for both the Lecturer / Senior Lecturer and the Research Associate positions. If positions are not filled after this round of recruitment, we will initiate another round of recruitment.

 

We also welcome enquiries from individuals who have obtained or are applying for their own postdoctoral fellowships, e.g., Marie Sklodowska-Curie Fellowships. For suitable candidates we would assist with the application process.

 

Potential candidates are encouraged to contact Dr Geoffrey Stewart Morrison to seek more information about these positions.

Tel:    +44 121 204 3901

e-mail:    g.s.morrison@aston.ac.uk

 

Dr Morrison will be attending Interspeech in September and would be happy to meet informally with potential applicants there.

 

Please visit our website http://www.aston.ac.uk/jobs for further information and to apply online.

 

Aston University is an equal opportunities employer and welcomes applications from all sections of the community.

Top

6-12(2019-08-14) Postdoc at KTH, Stockholm, Sweden
We are looking for a postdoc to conduct research in a multidisciplinary expedition project funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden?s largest individual research program, addressing compelling research topics that promise disruptive innovations in AI, autonomous systems and software for several years to come.
 
The project combines Formal Methods and Human-Robot Interaction with the goal of moving from conventional correct-by-design control with simple, static human models towards the synthesis of correct-by-design and socially acceptable controllers that consider complex human models based on empirical data. Two demonstrators, an autonomous driving scenario and a mobile robot navigation scenario in crowded social spaces, are planned to showcase the advances made in the project.
 
The focus of this position is on the development of data-driven models of human behavior that can be integrated with formal methods-based systems to better reflect real-world situations, as well as in the evaluation of the social acceptability of such systems. 
 
The candidate will work under the supervision of Assistant Prof. Iolanda Leite (https://iolandaleite.com/) and in close collaboration with another postdoctoral researcher working in the field of formal synthesis.
 
This is a two-year position. The starting date is open for discussion, but ideally, we would like the selected candidate to start ASAP.
 
 
QUALIFICATIONS
 
Candidates should have completed, or be near completion of, a Doctoral degree with a strong international publication record in areas such as (but not limited to) human-robot interaction, social robotics, multimodal perception, and artificial intelligence. Familiarity with formal methods, game theory, and control theory is an advantage.
 
Documented written and spoken English and programming skills are required. Experience with experimental design and statistical analysis is an important asset. Applicants must be strongly motivated, be able to work independently and possess good levels of cooperative and communicative abilities.
 
We look for candidates who are excited about being a part of a multidisciplinary team.
 
 
HOW TO APPLY
 
The application should include:
 
1. Curriculum vitae.
2. Transcripts from University/ University College.
3. A brief description of the candidate's research interests, including previous research and future goals (max 2 pages).
4. Contact of two references. We will contact the references only for selected candidates.
 
The application documents should be uploaded using the KTH's recruitment system:
 
 
The application deadline is ** September 13, 2019 ** 
 
-----------------
Iolanda Leite
Assistant Professor
KTH Royal Institute of Technology
School of Electrical Engineering and Computer Science
Division of Robotics, Perception and Learning (RPL)

Teknikringen 33, 4th floor, room 3424, SE-100 44 Stockholm, Sweden
Phone: +46-8 790 67 34
https://iolandaleite.com
 
Top

6-13(2019-08-17) Fully funded PhD position at IDIAP, Martigny, Valais, Switzerland.

There is a fully funded PhD position open at Idiap Research Institute on spiking neural
architectures for speech prosody.

The research will build on work done recently at Idiap on creating tools for
physiologically plausible modelling of speech. The current 'toolbox' contains rudimentary
muscle models and means to drive these using conventional (deep) neural networks. The
main focus of the work will involve use of spiking neural networks such as the 'integrate
and fire' type that is broadly representative of those found in biological systems.
Whilst we have focused so far on prosody (actually intonation), the application is open
ended; the focus is on the neural modelling. A key problem to be solved will be that of
training of the spiking networks, especially with the recurrence that is usual in such
networks. We hope to be able to train and use spiking networks as easily as conventional
backpropagation networks, and to shed light on current understanding of how biological
spiking networks learn (e.g., via spike timing-dependent plasticity).

For more information, and to apply, please follow this link:
 http://www.idiap.ch/education-and-jobs/job-10263

Idiap is located in Martigny in French speaking Switzerland, but functions in English and
hosts many nationalities.  PhD students are registered at EPFL. All positions offer quite
generous salaries.  Martigny has a distillery and a micro-brewery and is close to all
manner of skiing, hiking and mountain life.

There are other open positions on Idiap's main page
 https://www.idiap.ch/en/join-us/job-opportunities

Top

6-14(2019-08-18) PhD positions at IRIT, Toulouse, France

Applications are invited for a three-year Early Stage Researcher PhD positions in the speech technology for pathological speech.

Description
The thesis focuses on studying the link between the internal representations of Deep Neural Networks (DNNs) and the subjective representation of speech intelligibility. We propose to explore the saliency detection capabilities of DNNs when used in a regression task for predicting speech intelligibility scores as given by human experts. By saliency, we mean to retrieve which frequency bands are important and used by a DNN to make its predictions.
 
The final expectation is to identify regions of interest in the speech signal, both in time and frequency, that characterise the level of speech impairment.
 
The experiments will be processed on various samples of speech performed by 150 people (100 patients and 50 healthy controls). This database was recorded within the INCA C2SI project, and contains speech from patients treated for cancer of the oral cavity or pharynx. It contains also various metadata such as the location of the tumor, the impairment in terms of severity and intelligibility that were appreciated by human experts, self evaluation questionnaires on the patient?s quality of life? Various tasks were recorded such as a sustained vowel, read speech, nonsense words, prosodic exercises, picture description, etc.
There will be also the possibility to extend the work to another corpus which is composed of voice of patients suffering from Parkinson disease.
 
At first, the PhD will have to take benefit from the various analysis and descriptions that were done during the C2SI project trying to correlate the impact of the tumor and the communication ability. Those results will help attesting the human representation of the impact of the disease. Then, a DNN representation will be modeled to fit the data, taking care of the data sparsity. The last part of the work will be to explore the intern representation of the DNN, trying to explore what part of the signal help to make a decision on the impact of the disease and that will be the final goal of the thesis, studying the automatic representation that lies in the model the student will propose.
 
This work is funded by the TAPAS project (https://www.tapas-etn-eu.org) which is a Horizon 2020 Marie Sk?odowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project that aims to transform the well being of people across Europe with debilitating speech pathologies (e.g., due to stroke, Parkinson's, etc.). These groups face communication problems that can lead to social exclusion. They are now being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but which is not robust to atypical speech.
 
 
The supervision of the PhD will take place at IRIT laboratory by the SAMoVA team in Toulouse. SAMoVA does research in the domain of ?analysis, modeling and structuring of audiovisual content?. The application areas are diverse: speech processing, identification of languages, speaker verification and speech and music indexing. The researchers expertise covers novel machine learning and audio processing technologies and is now focused on deep learning methods, leading to several publications in international conferences.
 

Eligibility Criteria:

 

Early Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers.

- The ESR may be a national of a Member State, of an Associated Country or of any Third Country.
- The ESR must not have resided or carried out her/his main activity (work, studies, etc.) in the country of her/his host organization for more than 12 months in the 3 years immediately prior to her/his recruitment.
- Holds a Master?s degree or equivalent, which formally entitles to embark on a Doctorate.
- Does not hold a PhD degree.


Duration of recruitment: 36 months.

 
Applications can be done through the website : https://www.tapas-etn-eu.org/positions/recruitment
Contact : Julie Mauclair (mauclair@irit.fr)
Top

6-15(2018-08-25) Post-doc position at INRIA Rennes, France

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

Advisors:  Elisa Fromont & Alexandre Termier, IRISA/INRIA RBA ? Lacodam team (Rennes)

Irina Illina & Emmanuel Vincent, LORIA/INRIA  ? Multispeech team (Nancy)
firstname.lastname@inria.fr

Location: INRIA RBA, team Lacodam (Rennes)

Keywords: discriminative pattern mining, neural networks analysis, explainability of black
box models, speech recognition.

Deadline to apply: September 30th, 2019

Context:

Understanding the inner working of deep neural networks (DNN) has attracted a lot of  attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4].  Those techniques help to understand what an individual neuron  or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.

In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.

Description of the position:

This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminativepattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.

The data will be provided by the MULTISPEECH team and will consist of two deep architectures as  representatives of acoustic and language models [9, 10]. Furthermore, the training data will be  provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.

The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team  (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year) post-doc with the following preferred skills:
? Some knowledge (interest) about speech recognition
? Knowledgeable in pattern mining (discriminative pattern mining is a plus)
? Knowledgeable in machine learning in general and deep learning particular
? Good programming skills in Python (for Keras and/or Tensor Flow)
? Very good English (understanding and writing)

 See the INRIA web site for the post-doc page.

The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in September for an expected start in October-November 2019.

Bibliography:

[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).

[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.

[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.

[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.

[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.

[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and  Knowledge Discovery in Databases, pages 206?223. Springer, 2015.

[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.

Top

6-16(2019-08-28) Speech technologist/linguist at Cobaltspeech.

Cobalt Speech & Language (http://www.cobaltspeech.com/ ) is looking for a speech technologist/linguist to help find and create language resources for a project in French Canadian.

The project is short term (<2 months) part time (~5-8h  a week) which is ideal for a student to get an experience with speech industry.

The following skills are required:
  - Native French (does not have to be Canadian - though desirable)
  - Able to communicate in English
  - Basic understanding of speech technology and linguistics
  - Ability to run a python script.

For more information, please contact (Rasmus Dall) rasmus@cobaltspeech.com

Top

6-17(2019-09-04)PhD thesis proposal, GIPSA Lab Grenoble France

PhD thesis proposal

Incremental sequence-to-sequence mapping for

speech generation using deep neural networks

September 4, 2019

1 Context and objectives

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

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

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

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

Recurrent, convolutional and transformer architectures, coupled with attention

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

pendencies between a source and a target sequence of multidimensional discrete

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

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

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

waveform, etc.).

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

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

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

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

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

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

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

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

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

hence limiting the dynamics and naturalness of the interaction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

be replaced on-the-y with reconstructed portions.

2 Work plan

The proposed working plan is the following :

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

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

(low-latency) variations

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

cremental S2S models.

 Implementing and evaluating the proposed techniques in the context of

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

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

 Implementing and evaluating the proposed techniques in the context of

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

on pathological speech.

3 Requirements

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

on this subject. Following requirements are mandatory:

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

Processing or Applied Mathematics.

 Solid skills in Machine Learning. General knowledge in natural language

processing and/or speech processing.

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

works).

 Good oral and written communication in English.

 Ability to work autonomously and in collaboration with supervisors and

other team members.

2

4 Work context

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

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

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

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

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

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

mentary activity or not).

5 How to apply?

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

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

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

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

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

evaluated as they are received: the position is open until it is filled.

Top

6-18(2019-09-04) Postdoc proposal, GIPSA Lab Grenoble, France

Postdoc proposal

Spontaneous Speech Recognition.

Application to Situated Corpora in French.

September 4, 2019

1 Postdoc Subject

The goal of the project is to advance the state-of-the-art in spontaneous auto-

matic speech recognition (ASR). Recent advances in ASR show excellent per-

formances on tasks such as read speech ASR (Librispeech), TV shows (MGB

challenge), but what about spontaneous communicative speech ?

This postdoc project would leverage existing transcribed corpora in French

(more than 300 hours) recorded in everyday communication (speech recordings

inside a family, in a shop, during an interview, etc.). One impact of the project

would be the automatization of transcription on very challenging data in order

to feed linguistic and phonetic studies at scale.

Research topics:

 End-to-end ASR models

 Spontaneous speech ASR

 Colloquial speech transcription

 Data augmentation for spontaneous and colloquial language modelling

 Transcribing situated corpora

2 Requirements

We are looking for an outstanding and highly motivated postdoc candidate to

work on this subject. Following requirements are mandatory:

 PhD degree in natural language processing or speech processing.

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

works).

1

 Interest in pluri-disciplinary research (speech technology and speech sci-

ence)

 Good oral and written communication in English (French is a plus while

not mandatory)

 Ability to work autonomously and in collaboration with other team mem-

bers.

3 Work context

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

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

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

Institute (MIAI). The candidate will work both at LIG-lab (GETALP team)

and LIDILEM-lab. The duration of the postdoc is 18 months.

4 How to apply?

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

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

one-page summary of the PhD thesis. Applications should be sent to lau-

rent.besacier@imag.fr Applications will be evaluated as they are received: the

position is open until it is lled.

Top

6-19(2019-09-24) VOXCRIM 2019, Ecully France

nceVOXCRIM 2019

 

 

MARDI 24 SEPTEMBRE 2019

 

de 9h30 à 17h00

 

Conférences et table ronde :

 

regards croisés sur la comparaison de voix

 

en criminalistique.

 

Inscriptions avant le 13 septembre

 

voxcrim@interieur.gouv.fr

 

04 72 86 85 22

 

Service Central de la Police

 

Technique et Scientifique

 

31 avenue Franklin Roosevelt

 

69130 ECULLY

 

Top

6-2052019-09-05) Post doctoral position at IDIAP, Martigny, Switzerland

The Social Computing Group at Idiap is seeking a creative and motivated postdoctoral
researcher to work on deep learning methods for behavioral analysis from video and audio
data. This is an opening for a researcher with experience in deep learning applied to
dynamic human behavior (from voice, body, or face), in the context of a project funded by
Innosuisse, the Swiss funding agency for promotion of innovation.

The position offers the opportunity to do exciting work on deep learning and social
behavior. The researcher will work with Prof. Daniel Gatica-Perez and his research group.
The candidates will have a PhD degree in computer science or engineering, with proven
experience in deep learning and a strong publication record.

Salaries are competitive and starting date is immediate. Interviews will start upon
reception of applications until the positions are filled.

Interested candidates are invited to submit a cover letter, a detailed CV, and the names
of three references through Idiap's online recruitment system:

https://www.idiap.ch/en/join-us/job-opportunities
Position: Postdoctoral researcher in deep learning for social behavior analysis

Interested candidates can also contact Prof. Daniel Gatica-Perez (gatica@idiap.ch).

About Idiap Research Institute

Idiap is an independent, not-for-profit, research institute recognized and funded by the
Swiss Federal Government, the State of Valais, and the City of Martigny. Idiap is an
equal opportunity employer, and offers competitive salaries and excellent working
conditions in a dynamic and multicultural environment.

Idiap is located in the town of Martigny in Valais, a scenic region in the south of
Switzerland, surrounded by the highest mountains of Europe, and offering exceptional
quality of life, exciting recreational activities, including hiking, climbing and skiing,
as well as varied cultural activities, all within close proximity to Lausanne and Geneva.
English is the official working language.

Delete | Reply | Reply to All | Forward | Redirect | View Thread | Blacklist | Whitelist | Message Source | Save as | Print
Move | Copy
Top

6-21(2019-09-09) Postdoctoral Research Fellow/Senior Research Fellow, University of Tampere, Finland

Postdoctoral Research Fellow/Senior Research Fellow

(speech and language technology, cognitive science)

Tampere University and Tampere University of Applied Sciences create

a unique environment for multidisciplinary, inspirational and highimpact

research and education. Our universities community has its

competitive edges in technology, health and society. www.tuni.fi/en

Speech and Cognition research group (SPECOG) is part of Computing Sciences Unit of Tampere University

within the Faculty of Information Technology and Communication Sciences. SPECOG focuses on

interdisciplinary research at the intersection of speech technology and cognitive sciences. We apply

advanced signal processing and machine learning methods to computational modeling of human language

learning and perception and study how human-like information processing principles can be applied in

autonomous next-generation artificial intelligence (AI) systems. The group also conducts research and

development in speech and language technology and in medical signal processing and machine learning.

SPECOG collaborates with several internationally leading research groups within and across disciplinary

boundaries, including joint research with computer scientists, psychologists, brain researchers, and linguists.

The group is also closely affiliated with audio and machine vision research groups of Tampere University.

More information on SPECOG: http://www.cs.tut.fi/sgn/specog/index.html

Job description

We are inviting applications for the position of a postdoctoral research fellow or senior research fellow in

the areas of speech and language technology and cognitive science. The work will be conducted as a

member of the SPECOG research group led by Asst. Prof. Okko Räsänen. We are looking for candidates who

are interested in human and/or machine language processing, and who are willing to contribute to our

highly cross-disciplinary research efforts in understanding language learning in humans and autonomous

computational systems. Our current focus is on machine learning algorithms for unsupervised language

learning from purely acoustic or audiovisual data (sometimes also known as zero-resource speech

processing). However, we also consider candidates with a strong independent research agenda in

complementary areas of speech and language technology.

In this position, the candidate is expected to:

1) carry out world-class research on a topic related to SPECOG focus areas

2) work in close collaboration with other members of the research group, and

3) help to advise undergraduate and/or PhD projects on the relevant topics (with flexibility according to

personal interests and career aspirations).

Requirements

The candidate should hold a doctoral degree (e.g., PhD or D.Sc. (Tech.)) in language technology, computer

science, electrical engineering, cognitive science, or other relevant area. Candidates who have already

completed their doctoral research work but have not yet received their doctoral certificate may also apply.

A successful candidate has strong expertise in signal processing and machine learning (e.g., deep learning),

ideally from the context of speech technology. Applicants with a background in natural language processing

(NLP) or cognitive science are also considered. Experience or interests in linguistics, neuroscience, or

statistics are considered as an advantage. Fluent programming (Python, Matlab, R, C++ or similar) and

English skills are required.

Potential candidates must be capable of carrying out independent research at the highest international

level. Competence must be demonstrated through several existing publications in internationally

recognized peer-reviewed journals and conferences.

We offer

The position will be filled for a fixed-term period of two years, starting as soon as possible (but not

extending the contract beyond the end of December 2021). A trial period of 6 months is applied to all new

employees. The exact starting date is negotiable.

We offer competitive academic salary, typically between 3500–4000 € for a starting postdoc depending on

the experience of the candidate, and 4000–4500 € for a senior research fellow with several years of existing

postdoctoral research experience in academia or industry. In addition, the position comes with extensive

benefits such as occupational healthcare, excellent sports facilities, flexible working hours, and several

restaurants and cafés on the campus with staff discounts. Traveling costs and daily allowances related to

presenting peer-reviewed work in major international conferences is also normally covered.

How to apply

Send the application through the online portal at https://tuni.rekrytointi.com/paikat/?o=A_A&jid=301

We will accept applications until the position has been filled, but no later than 30th of November 2019 at

23.59 (GMT+3). Note that we will start evaluating the applicants already on 1st of October 2019, and the

position may be filled as soon as a suitable candidate is found. We reserve the opportunity to recruit the

candidate through other channels or to decide to not to fill the position in case a suitable candidate is not

found during the process.

The application should contain the following documents (all in .pdf format):

- A free-form letter of motivation for the position in question (max. 1 page)

- Academic CV with contact information

- A list of publications

- A copy of doctoral degree certificate

- A letter or letters of recommendation (max. 3)

Please name all the documents as surname_CV.pdf, surname_list_of_publications.pdf … etc. Only the

applications sent through the university application portal and containing the requested attachments in the

instructed format will be considered in the recruitment process.

The most promising candidates will be interviewed in person or via Skype before the final decision.

For more information about the position, please contact Assistant Professor Okko Räsänen

(firstname.surname@tuni.fi; no umlauts) by email.

About the research environment

Finland is among the most stable, free and safe countries in the world, based on prominent ratings by

various agencies. It is also ranked as one of the top countries as far as social progress is concerned.

Tampere is counted among the major academic hubs in the Nordic countries and offers a dynamic living

environment. Tampere region is one of the most rapidly growing urban areas in Finland and home to a

vibrant knowledge-intensive entrepreneurial community. The city is an industrial powerhouse that enjoys a

rich cultural scene and a reputation as a centre of Finland’s information society. Despite its growth, living in

Tampere is highly affordable with two-room apartment rents starting from approx. 550 €. In addition, the

excellent public transport network enables quick, easy and cheap transportation around the city of

Tampere and university campuses.

Read more about Finland and Tampere:

https://www.visitfinland.com/about-finland/

https://finland.fi/

http://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/161193/MEAEguide_18_2018_T

ervetuloaSuomeen_Eng_PDFUA.pdf

https://visittampere.fi/en/

Top

6-22(2019-09-09) Doctoral Researcher; UNiversity of Tampere, Finland

Doctoral Researcher

(speech and language technology, cognitive science)

Tampere University and Tampere University of Applied Sciences create

a unique environment for multidisciplinary, inspirational and highimpact

research and education. Our universities community has its

competitive edges in technology, health and society. www.tuni.fi/en

Speech and Cognition research group (SPECOG) is part of Computing Sciences Unit of Tampere University

within the Faculty of Information Technology and Communication Sciences. SPECOG focuses on

interdisciplinary research at the intersection of speech technology and cognitive sciences. We apply

advanced signal processing and machine learning methods to computational modeling of human language

learning and perception and study how human-like information processing principles can be applied in

autonomous next-generation artificial intelligence (AI) systems. The group also conducts research and

development in speech and language technology and in medical signal processing and machine learning.

SPECOG collaborates with several internationally leading research groups within and across disciplinary

boundaries, including joint research with computer scientists, psychologists, brain researchers, and linguists.

The group is also closely affiliated with audio and machine vision research groups of Tampere University.

More information on SPECOG: http://www.cs.tut.fi/sgn/specog/index.html

Job description

We are inviting applications for the position of a doctoral researcher (doctoral student) in the areas of

speech and language technology and cognitive science. The work will be conducted as a member of the

SPECOG research group led by Asst. Prof. Okko Räsänen. We are looking for candidates who are interested

in human and/or machine language processing, and who are willing to contribute to our highly crossdisciplinary

research efforts in understanding language learning in humans and autonomous computational

systems. Our current focus is on machine learning algorithms for unsupervised language learning from

purely acoustic or audiovisual data (sometimes also known as zero-resource speech processing). However,

we also consider candidates with interest towards complementary areas of speech and language

technology.

In this position, the candidate is expected to:

1) carry out research on a mutually agreed topic

2) complete a doctoral degree, including mandatory course studies for a D.Sc. (tech.) degree

3) participate to doctoral program

4) be available for assisting tasks in teaching and research group activities (max. 15% of working time)

Requirements

The candidate should hold a master’s degree in language technology, computer science, electrical

engineering, mathematics, cognitive science, or other relevant technical area. Candidates who have already

completed their master’s studies but are graduating during 2019 may also apply. Exceptional master’s

students of Tampere University, who are close to graduation, can be also considered for the position. In

this case, the candidate is first employed as a Research Assistant to carry out a master’s thesis project (6

months) on the topic and, upon a successful thesis project, with the possibility to continue to doctoral

studies.

A successful candidate has experience from signal processing and/or machine learning (e.g., deep learning),

ideally from the context of speech technology. Applicants with a background in natural language processing

(NLP) or cognitive science are also considered. Experience or interests in linguistics, neuroscience, or

statistics are considered as an advantage but not required. Good command of programming (Python,

Matlab, R, C++ or similar) and English skills are required.

Potential candidates must be capable of carrying out independent research work but are also good team

players. Previous experience from research such as research internships or other research projects are

considered as a significant advantage.

We offer

The position will be filled for a fixed-term period of two years with a view for extension. A trial period of 6

months is applied to all new employees. The position start in January 2020 or as soon as possible with a

negotiable exact starting date. Target completion time for doctoral studies is 4 years.

We offer a starting salary of 2300 € for a starting doctoral researcher with later increases based on

demonstrated progress through scientific publications and acquired study credits. In addition, the position

comes with extensive benefits such as occupational healthcare, excellent sports facilities, flexible working

hours, and several restaurants and cafés on the campus with staff discounts. Traveling costs and daily

allowances related to presenting peer-reviewed work in major international conferences is also normally

covered.

How to apply

Send your application through the online portal at https://tuni.rekrytointi.com/paikat/?o=A_A&jid=299

We will accept applications until 15th of November 2019 at 23.59 (GMT+3). We reserve the opportunity to

recruit the candidate through other channels or to decide to not to fill the position in case a suitable

candidate is not found during the process.

The application should contain the following documents (all in .pdf format):

- A free-form letter of motivation for the position in question (max. 1 page)

- Complete CV with contact information and a list of publications (if any)

- A copy of master’s degree certificate

- English language certificate of proficiency (for non-native and non-Finnish applicants)

Please name all the documents as surname_CV.pdf, surname_list_of_publications.pdf … etc. Only the

applications sent through the university application portal and containing the requested attachments in the

instructed format will be considered in the recruitment process.

The most promising candidates will be interviewed in person or via Skype before the final decision.

For more information about the position, please contact Assistant Professor Okko Räsänen

(firstname.surname@tuni.fi; no umlauts) by email.

About the research environment

Finland is among the most stable, free and safe countries in the world, based on prominent ratings by

various agencies. It is also ranked as one of the top countries as far as social progress is concerned.

Tampere is counted among the major academic hubs in the Nordic countries and offers a dynamic living

environment. Tampere region is one of the most rapidly growing urban areas in Finland and home to a

vibrant knowledge-intensive entrepreneurial community. The city is an industrial powerhouse that enjoys a

rich cultural scene and a reputation as a centre of Finland’s information society. Despite its growth, living in

Tampere is highly affordable with private market two-room apartment rents starting from approx. 550 €. In

addition, the excellent public transport network enables quick, easy and cheap transportation around the

city of Tampere and university campuses.

Read more about Finland and Tampere:

https://www.visitfinland.com/about-finland/

https://finland.fi/

http://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/161193/MEAEguide_18_2018_T

ervetuloaSuomeen_Eng_PDFUA.pdf

https://visittampere.fi/en/

Top

6-23(2019-09-09) Postdoc position at IRIT, Toulouse, France

Projet READYNOV : AUDIOCAP

Audition et handicap dans le bruit : vers la restauration de l’intelligibilité de la parole

Type d’emploi POSTDOC

Cadre de la recherche

Restauration d’une intelligibilité dans le bruit pour les personnes

âgées via des prothèses auditives.

Mots-clés Parole, bruit, intelligibilité

Missions

Prédiction de l’intelligibilité de la parole dans le bruit :

- Prise en main d’un système de Reconnaissance Automatique de

la Parole en Français,

- Modélisation acoustique dans le bruit.

Mise en place d’un outil de séparation de la parole et du bruit, fondée sur

l’application de filtres temps-fréquences. Celui-ci sera « réglé » dans un

but de favoriser l’intelligibilité de la parole.

Compétences

Développement logiciel

Traitement du signal

Apprentissage machine (« deep learning »)

Lieu IRIT – 118, route de Narbonne – 31062 TOULOUSE

Date et durée de la mission De 12 à 18 mois à partir du 1er octobre 2019

Salaire Entre 1900 et 2400 € net par mois, suivant l’expérience

Documents à fournir

- CV détaillé

- Lettre de motivation

- Résumé d'une page de la thèse de doctorat

Contact Julien PINQUIER, pinquier@irit.fr

Top

6-24(2019-09-05) R/D position at Zaion, Paris France

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

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

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

Nous rejoindre, c’est prendre part à une aventure passionnante et innovante afin de devenir la référence sur le marché des robots conversationnels. Dans le cadre de son développement ZAION recrute son Data Scientist /Machine Learning appliqué à l’Audio H/F. Au sein de l’équipe R&D, votre rôle est stratégique dans le développement et l’expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu’ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse…) et donc adapter leurs réponses en conséquence.

Vos missions principales :

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

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

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

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

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

- Déployez vos modèles en production

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

Qualifications requises et expérience antérieure :

-Vous avez une expérience de 2 ans minimum comme Data Scientist/Machine Learning appliqué à l’Audio

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

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

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

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

- Vous maîtrisez les techniques du traitement du signal audio

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

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

- Vous parlez anglais couramment

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

Top

6-25(2019-09-15) Post-doc and research engineer at INSA, Rouen, Normandy, France

Post-doctoral position (1 year): Perception for interaction and social navigation

Research Engineer (1 year): Social Human-Robot Interactions

Laboratory: LITIS, INSA Rouen Normandy, France

Project: INCA (Natural Interactions with Artificial Companions)

Summary:

The emergence of interactive robots and connected objects has lead to the appearance of symbiotic systems made up of human users, virtual agents and robots in social interactions. However, two major scientific difficulties are unsolved yet: on the one hand, the recognition of human activity remains inaccurate, both at the operational level (location, mapping and identification of objects and users) and cognitive (recognition and tracking of users? intentions) and, on the other hand, interaction involves different modalities that must be adapted according to the context, the user and the situation. The INCA project aims at developing artificial companions (interactive robots and virtual agents) with a particular focus on social interactions. Our goal is to develop new models and algorithms for intelligent companions capable of (1) perceiving and representing an environment (real, virtual or mixed) consisting of objects, robots and users; (2) interacting with users in a natural way to assess their needs, preferences, and engagement; (3) learning models of user behavior and (4) generating semantically adequate and socially appropriate responses.

 

Post-doctoral position in perception for interaction and social navigation (1 position)

The candidate will work to ensure that a robot can recognize the physical content of the scene surrounding him, recognize himself, static and dynamic objects (users and other robots) and finally predict the movement of dynamic elements. The integration of data from different sensors should allow the mapping of an unknown environment and estimate the position of the robot. First, VSLAM techniques (Visual Simultaneous Localization And Mapping) (Saputra 2018) will be used to map the scene. The regions (or points) of interest detected could
then be used to detect obstacles. In order to distinguish between static and dynamic objects, methods of separating the background from the foregound of the scene (Kajo et al, 2018) will be used. Finally, some recent techniques of the Flownet 2.0 type (Eddy et al, 2017), for the prediction of the motion on a video sequence should make it possible to predict the next movement of an object dynamic object and the to apprehend its behavior.

Profile: the candidate must have strong skills in mobile robotics and navigation techniques (VSLAM, OrbSlam, Optical Flow, stereovision...) and a high programming capacities under ROS or any other programming language compatible with robotics. Machine learning and Deep learning skills will be highly appreciated.

Research Engineer in Social Human-Robot Interactions (1 position)

The hired research engineer will work closely with the INCA research staff (permanent, PhD and post-doctoral members) and other project partners. This will mainly involve administering the project's Pepper robots, developing the necessary tools, integrating the algorithms developed with the AgentSlang platform (https://agentslang.github.io/) and join the team created to participate in the Robocup 2020 in Bordeaux, @Home league.

Profile: Computer Sciences / Robotics Engineer

  • Good level in programming (ROS, Python, possibly Java)

  • Strong knowledge in robotics

  • Experiences in some of the following areas would be a plus (non-exhaustive list): machine learning, human-machine social interactions, scene perception, spatio-temporal and semantic representation, natural language dialogue.

Duration and remuneration: 1 year, 2480euros/month (gross salary)

Application should be sent to: alexandre.pauchet@insa-rouen.fr

  • Curriculum vitae

  • Cover letter

  • Recommendation letters

  • Recently graduated students: transcripts

Top

6-26(2019-09-20) Poste ATER, Paris Sorbonne, France

un poste d'ATER en Informatique est disponible à la faculté des lettres de Sorbonne
Université. Le lien pour postuler est http://lettres.sorbonne-universite.fr/ater

Top

6-27(2019-09-21) Post-doc/PhD position, LORIA, Nancy, France
Post-doc/PhD position Pattern mining for Neural Networks debugging: application to speech recognition
 
Advisors:  Elisa Fromont & Alexandre Termier, IRISA/INRIA RBA ? Lacodam team (Rennes)
Irina Illina & Emmanuel Vincent, LORIA/INRIA  ? Multispeech team (Nancy)

firstname.lastname@inria.fr

Location: INRIA RBA, team Lacodam (Rennes)

Deadline to apply : October 30th 2019.
 
Starting date : December 2019 -January 2020
 
Keywords: discriminative pattern mining, neural networks analysis, explainability of blackbox models, speech recognition.

Context:
Understanding the inner working of deep neural networks (DNN) has attracted a lot of  attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4].  Those techniques help to understand what an individual neuron  or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.

In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.

Description of the position:

This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminative pattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.

The data will be provided by the MULTISPEECH team and will consist of two deep architectures as  representatives of acoustic and language models [9, 10]. Furthermore, the training data will be  provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.

The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team  (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year) post-doc with the following preferred skills:

? Some knowledge (interest) about speech recognition
? Knowledgeable in pattern mining (discriminative pattern mining is a plus)
? Knowledgeable in machine learning in general and deep learning particular
? Good programming skills in Python (for Keras and/or Tensor Flow)
? Very good English (understanding and writing)

However, good PhD applications will also be considered and, in this case, the position will last 3 years. The position will be funded by INRIA (https://www.inria.fr/en/). See the INRIA web site for the post-doc and PhD wages.

The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in June for an expected start in September 2019.

Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and  Knowledge Discovery in Databases, pages 206?223. Springer, 2015
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
[8] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
[9] G. Saon, H.-K. J. Kuo, S. Rennie, M. Picheny: The IBM 2015 English conversational telephone speech recognition system?, Proc. Interspeech, pp. 3140-3144, 2015.
[10] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke : The Microsoft 2017 Conversational Speech Recognition System, IEEE ICASSP, 2018.
 
Top

6-28(2019-09-22) Postdoc position at Grenoble Alps University, Grenoble, France

Postdoc proposal

Spontaneous Speech Recognition.

Application to Situated Corpora in French.

September 4, 2019

1 Postdoc Subject

The goal of the project is to advance the state-of-the-art in spontaneous auto-

matic speech recognition (ASR). Recent advances in ASR show excellent per-

formances on tasks such as read speech ASR (Librispeech), TV shows (MGB

challenge), but what about spontaneous communicative speech ?

This postdoc project would leverage existing transcribed corpora in French

(more than 300 hours) recorded in everyday communication (speech recordings

inside a family, in a shop, during an interview, etc.). One impact of the project

would be the automatization of transcription on very challenging data in order

to feed linguistic and phonetic studies at scale.

Research topics:

 End-to-end ASR models

 Spontaneous speech ASR

 Colloquial speech transcription

 Data augmentation for spontaneous and colloquial language modelling

 Transcribing situated corpora

2 Requirements

We are looking for an outstanding and highly motivated postdoc candidate to

work on this subject. Following requirements are mandatory:

 PhD degree in natural language processing or speech processing.

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

works).

1

 Interest in pluri-disciplinary research (speech technology and speech sci-

ence)

 Good oral and written communication in English (French is a plus while

not mandatory)

 Ability to work autonomously and in collaboration with other team mem-

bers.

3 Work context

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

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

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

Institute (MIAI). The candidate will work both at LIG-lab (GETALP team)

and LIDILEM-lab. The duration of the postdoc is 18 months.

4 How to apply?

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

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

one-page summary of the PhD thesis. Applications should be sent to lau-

rent.besacier@imag.fr Applications will be evaluated as they are received: the

position is open until it is lled.

Top

6-29(2019-09-22) PhD thesis proposal at Grenble Alps University, Grenoble, France

PhD thesis proposal

Incremental sequence-to-sequence mapping for

speech generation using deep neural networks

September 4, 2019

1 Context and objectives

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

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

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

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

Recurrent, convolutional and transformer architectures, coupled with attention

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

pendencies between a source and a target sequence of multidimensional discrete

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

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

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

waveform, etc.).

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

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

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

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

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

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

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

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

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

hence limiting the dynamics and naturalness of the interaction.

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

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

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

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

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

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

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

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

1

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

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

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

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

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

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

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

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

be replaced on-the-y with reconstructed portions.

2 Work plan

The proposed working plan is the following :

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

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

(low-latency) variations

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

cremental S2S models.

 Implementing and evaluating the proposed techniques in the context of

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

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

 Implementing and evaluating the proposed techniques in the context of

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

on pathological speech.

3 Requirements

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

on this subject. Following requirements are mandatory:

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

Processing or Applied Mathematics.

 Solid skills in Machine Learning. General knowledge in natural language

processing and/or speech processing.

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

works).

 Good oral and written communication in English.

 Ability to work autonomously and in collaboration with supervisors and

other team members.

2

4 Work context

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

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

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

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

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

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

mentary activity or not).

5 How to apply?

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

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

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

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

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

evaluated as they are received: the position is open until it is lled.

Top

6-30(2019-10-18) Journées d’étude sur la convergence, LPL, Aix en Provence, France

Journées d’étude sur la convergence

18-19 octobre 2019
Laboratoire Parole et Langage, Aix-en-Provence

Organisées par
Le Cercle Linguistique d’Aix-en-Provence (CLAIX)
L’équipe Systèmes & Usages du Laboratoire Parole et Langage (LPL)

Contacts : Sibylle Kriegel et Sophie Herment (LPL/AMU)

Page Web

Programme

Vendredi 18 octobre 2019

9h30-10h15              Accueil

10h15-11h15           Debra Ziegeler, conférencière invitée (U. Sorbonne Nouvelle) : The future of already in Singapore English: a matter of selective convergence

11h15-11h45           Pause-café

11h45-12h30           Diana Lewis (AMU, LPL) : Grammaticalisation de lexème, de construction : deux cas de développement adverbial en anglais

12h30-14h15           Déjeuner

14h15-15h00          James German (AMU, LPL) : Linguistic adaptation as an automatic response to socio-indexical cues

15h00-15h45         Daniel Véronique (AMU, LPL) : L’« agglutination nominale » dans les langues créoles françaises : un exemple de convergence ?

15h45-16h15          Pause-Café

16h15-17h00          Chady Shimeen-Khan (U. Paris Descartes, CEPED) : Convergences et divergences à des fins discursives à travers l’usage des marqueurs discursifs chez les jeunes Mauriciens plurilingues

17h00-17h45         Sibylle Kriegel (AMU, LPL) : Créolisation et convergence : l’expression du corps comme marque du réfléchi

17h45-18h15          Charles Zaremba : Le CLAIX Cercle Linguistique d’Aix-en-Provence, retrospective

 

Samedi 19 octobre

10h-10h45                 Akissi Béatrice Boutin (ILA-UFHB, Abidjan-Cocody) : Réanalyses avec et sans convergence dans le plurilinguisme ivoirien

10h45-11h30             Massinissa Garaoun (AMU) : Convergence linguistique et cycles : le cas de la négation en arabe maghrébin et en berbère     

11h30-12h                  Pause-café

12h-12h45                 Nicolas Tournadre (AMU, LACITO) : Phénomènes de copie et de convergence dans les langues du Tibet et de l’Himalaya

12h45-13h30            Cyril Aslanov (AMU, LPL) : Convergence and secondary entropy in a macrodiachronic perspective

 

Top

6-31(2019-10-05) offre de post-doctorat au Laboratoire national de métrologie et d' essais (LNE) , Trappes, France

offre de post-doctorat au sein de l’activité « Evaluation des systèmes d’intelligence artificielle » du LNE :

 

https://www.lne.fr/fr/offre-emploi/post-doc-evaluation-systemes-evolutifs-locuteur-traduction

 

Le candidat retenu intégrera une équipe en forte croissance spécialisée en évaluation des systèmes d’IA, ainsi qu’un projet européen ambitieux portant sur les systèmes de traitement de la langue évolutifs (en traduction et en diarisation). La caractérisation des performances des systèmes intelligents capables de s’auto-améliorer au fur et à mesure de leur utilisation, par eux-mêmes et par interaction avec l’humain utilisateur, représente un véritable défi que ce post-doctorat propose de relever.

Top

6-32(2019-10-15) Post doc Portugal

   An open full-time Post-Doc employment

    position for 30 months in the context of our research project DyNaVoiceR

    which is supported by FCT (the Portuguese Foundation for Science and

    Technology).

   

    The official announcement can be found:

   

    English version:

http://www.eracareers.pt/opportunities/index.aspx?task=showAnuncioOportunities&jobId=118038&idc=1e

 

   

    Portuguese version:

http://www.eracareers.pt/opportunities/index.aspx?task=showAnuncioOportunities&jobId=118038&lang=pt&idc=1e

 

    The salary level is quite attractive: 2.128,34 Euros per month (14

    salaries per year)

   

Aapplication deadline is October 15.

Top

6-33(2019-10-10) Ingenieur de recherche,Lab.national de metrologie et d'essais, Trappes, France

 

Ingénieur de recherche en

Traitement Automatique du Langage – F/H


Poste en CDI

Localisation : Laboeratoire national de metrologie et d'essais,Trappes

Traitement Automatique du Langage – F/H

Référence : ML/ITAL/DEC

Leader dans l’univers de la mesure et des références, jouissant d’une forte notoriété en France et à l’international, le LNE soutient l’innovation industrielle et se positionne comme un acteur important pour une économie plus compétitive et une société plus sûre.

Au carrefour de la science et de l’industrie depuis sa création en 1901, le LNE offre son expertise à l’ensemble des acteurs économiques impliqués dans la qualité et la sécurité des produits.

Pilote de la métrologie française, notre recherche est au coeur de cette mission de service public et l’une des clés du succès des entreprises.

Nous nous attachons à répondre au besoin industriel et académique de mesures toujours plus justes, dans des conditions de plus en plus extrêmes ou sur les concepts les plus émergents tels que les véhicules autonomes, les nanotechnologies ou la fabrication additive.

Missions :

Vous intégrerez une équipe de six ingénieurs-docteurs régulièrement accompagnés de post-doctorants, doctorants et stagiaires, spécialisée dans l’évaluation et la qualification des systèmes d’intelligence artificielle. Cette équipe est historiquement reconnue pour son expertise dans l’évaluation des systèmes de traitement automatique du langage naturel et le poste proposé doit contribuer à renforcer cette expertise dans un contexte de forte dynamique de croissance.

Depuis quelques années, l’équipe s’est diversifiée en termes de domaines d’application de son expertise d’évaluation des intelligences en traitant de sujets tels que les dispositifs médicaux, les robots industriels collaboratifs, les véhicules autonomes, etc. L’équipe capitalise sur les savoir-faire à la fois divers et ciblés de ses experts (TAL, imagerie, robotique, etc.) afin d’apporter conjointement une solution satisfaisante à la question de l’évaluation et de la certification des systèmes intelligents, condition impérative de leur acceptabilité et faisant l’objet aujourd’hui d’une attention prioritaire des pouvoirs publics.

C’est dans le cadre de la mise en place progressive d’un centre d’évaluation des systèmes intelligents à vocation nationale et internationale qu’elle cherche à accueillir les meilleurs profils de chaque spécialité de l’IA. Les missions principales de ce futur centre sont le développement de nouveaux protocoles d’évaluation, la qualification et la certification de systèmes intelligents, l’organisation de challenges (campagnes de benchmarking), la mise à disposition de ressources expérimentales, le développement et l’organisation du secteur d’activité et la définition de principes, politiques, doctrines et normes à cet effet.

En tant qu’ingénieur-docteur de recherche en TAL, votre champ d’intervention prioritaire sera le traitement de la langue (texte et parole). Vous pourrez également être amené.e à intervenir dans d’autres domaines du traitement de l’information (par exemple sur le traitement de l’image dont la reconnaissance optique de caractères), puis au-delà en fonction des priorités et de vos propres compétences et affinités.

Le poste est évolutif sur le moyen et le long termes en ce qu’il vise à la formation d’experts techniques de stature au moins nationale et ayant vocation à mener eux-mêmes la politique de croissance et de tutelle de leur spécialité, sous réserve du cadre réglementaire et d’orientations générales du LNE ou de ses donneurs d’ordres.

Dans un premier temps, vous couvrirez les missions suivantes :

- Contribution à la R&D et aux actions structurantes (60%) :

 

Inventaire technique et commercial du besoin et de l’offre, priorisation des marchés et champs techniques à investir

Identification et définition des grandeurs à mesurer, des métriques afférentes, des protocoles d’évaluation et des moyens d’essais nécessaires

Structuration des données de la discipline (TAL) au sein de référentiels et selon des nomenclatures à bâtir

Programmation et conduite d’essais à des fins expérimentales, de recherche itérative et d’étalonnage

Constitution et animation d’un réseau de chercheurs des secteurs public et privé, national et étranger, en appui aux présentes missions

Contribution au montage et à l’exécution de projets de recherche nationaux et européens et de coopérations internationales

Participation aux travaux de planification du LNE : investissements, RH, budgets annuels, perspectives pluriannuelles

Publication et présentation des résultats scientifiques

Encadrement éventuel de doctorants, post-doctorants, stagiaires

- Contribution aux prestations commerciales en TAL (40%) :

Ingénierie linguistique générale (manipulation des données, analyse statistique, etc.)

Prise en charge du besoin client et reformulation dans le cadre d’une offre technique et commerciale

Organisation des tâches pour la réalisation de la prestation, estimation des ressources nécessaires, négociation

Réalisation de ces tâches en coordination avec l’équipe

Production/rédaction des livrables

Présentation des résultats au client

Profil :

Vous êtes titulaire soit d’un doctorat, soit d’un diplôme d’ingénieur avec un minimum de trois ans d’expérience professionnelle, en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la langue (TAL) et plus généralement en intelligence artificielle. Les expériences professionnelles ou académiques passées en développement et/ou test logiciel, en analyse statistique, ainsi qu’en traitement de la parole ou de l’image seront particulièrement appréciées.

Vous disposez également d’un bon niveau d’anglais et de programmation (C++ et/ou Python), ainsi que d’une expérience en utilisation de Linux.

Dans le cadre de votre prise de poste, vous pourriez être amené.e à suivre des formations complémentaires (par exemple en intelligence artificielle et en cybersécurité).

Vous saurez être à l’initiative, en disposant d’une large autonomie et d’un potentiel de créativité vous permettant d’occuper pleinement votre espace de responsabilité dans un objectif d’excellence. Vous êtes capable de défendre un leadership de par la qualité et la clarté de vos argumentaires.

Déplacements fréquents en région parisienne (une fois par semaine), en province (une à deux fois par mois) et occasionnels dans le monde (une fois par trimestre) dans le cadre de prestations, réunions ou conférences.

Top

6-34(2019-10-13) Postdoctoral Researcher , IRISA, Rennes, France


Postdoctoral Researcher in Multilingual Speech Processing

CONTEXT The Expression research team focuses on expressiveness in human-centered data. In this context, the team has a strong activity in the field of speech processing, especially text-to-speech (TTS). This activity is denoted by regular publications in top international conferences and journals, exposing contributions in topics like machine learning (including deep learning), natural language processing, and speech processing. Team Expression takes part in multiple collaborative projects. Among those, the current position will take part in a large European H2020 project focusing on the social integration of migrants in Europe. Team’s website: https://www-expression.irisa.fr/

PROFILE Main tasks: 1. Design multilingual TTS models (acoustic models, grapheme-to-phoneme, prosody, text normalization...) 2. Take part in porting the team’s TTS system for embedded environments 3. Develop spoken language skill assessment methods

Secondary tasks: 1. Collect speech data 2. Define use cases with the project partners

Environment: The successful candidate will integrate a team of other researchers and engineers working on the same topics.

Required qualification: PhD in computer science or signal processing Skills: Statistical machine learning and deep learning Speech processing and/or natural language processing Strong object-oriented programming skills Android and/or iOS programming are a strong plus

CONTRACT Duration: 22 months, full time Salary: competitive, depending on the experience. Starting date: 1st, January 2020.

APPLICATION & CONTACTS Send a cover letter, a resume, and references by email to: Arnaud Delhay, arnaud.delhay@irisa.fr ; Gwénolé Lecorvé, gwenole.lecorve@irisa.fr ; Damien Lolive, damien.lolive@irisa.fr . Application deadline: 15th, November 2019. Applications will be processed on a daily basis.

Top

6-35(2019-10-16) Position in Machine Learning/AI at ReadSpeaker, The Netherlands

ReadSpeaker has a job opening for a Machine Learning / AI person working on text-to-speech research and development. Job ad can be found here:


https://www.readspeaker.com/careers/machine-learning-ai-for-text-to-speech-synthesis-research-and-development-to-deliver-business-solutions/

Top

6-36(2019-10-18) FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS, University Edingurgh, Scotland

FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

at the University of Edinburgh?s School of Informatics and School of Philosophy, Psychology and Language Sciences.

Applications are now sought for the CDT?s second cohort of students to start in September 2020

Deadlines:
* Non EU/UK : 29th November 2019
* EU/UK : 31st January 2020.

The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation. Both components run concurrently over four years.

Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP. They will receive full funding for four years, plus a generous allowance for travel, equipment and research costs.

The CDT brings together researchers in NLP, speech, linguistics, cognitive science and design informatics from across the University of Edinburgh. Students will be supervised by a team of over 40 world-class faculty and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs.

The CDT involves over 20 industrial partners, including Amazon, Facebook, Huawei, Microsoft, Mozilla, Reuters, Toshiba, and the BBC. Close links also exist with the Alan Turing Institute and the Bayes Centre.

A wide range of research topics fall within the remit of the CDT:

  • Natural language processing and computational linguistics

  • Speech technology

  • Dialogue, multimodal interaction, language and vision

  • Information retrieval and visualization, computational social science

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

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

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

  • Linguistic foundations of language and speech processing

The second cohort of CDT students will start in September 2020 and is now open to applications.

Around 12 studentships are available, covering maintenance at the research council rate (https://www.ukri.org/skills/funding-for-research-training , currently £15,009 per year) and tuition fees. Studentships are available for UK, EU and non-EU nationals. Individuals in possession of other funding scholarships or industry funding are also welcome to apply ? please provide details of your funding source on your application.

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

Further details including the application procedure can be found at: https://edin.ac/cdt-in-nlp

Application Deadlines
In order to ensure full consideration for funding, completed applications (including all supporting documents) need to be received by:

29th November 2019 (non EU/UK) or 31st January 2020 (EU/UK).

CDT in NLP Open Days
Find out more about the programme by attending the PG Open Day at the School of Informatics or by joining one of the CDT in NLP Virtual Open Days:

Enquiries can be made to the CDT admissions team at cdt-nlp-info@inf.ed.ac.uk

Top

6-37(2019-10-19) Postdoctoral Scholar, University South California, USA
Open Position - Postdoctoral Scholar for Multimodal Machine Learning and Natural Language Processing
 

The University of Southern California?s Institute for Creative Technologies (ICT) is an off-campus research facility, located on a creative business campus in the ?Silicon Beach? neighborhood of Playa Vista. We are world leaders in innovative training and education solutions, computer graphics, computer simulations, and immersive experiences for decision-making, cultural awareness, leadership and health.  ICT employees are encouraged to develop themselves both professionally and personally, through workshops, invited guest talks, movie nights, social events, various sports teams, a private gym and a personal trainer. The atmosphere at ICT is informal and flexible, while encouraging initiative, personal responsibility and a high work ethic.

We are looking for an accomplished recent PhD graduate to work on a challenging yet exciting NIH-funded 4-year research project.  The project seeks to understand the process and success of Motivational Interviewing (MI). Specifically, our project will address shortcomings of current MI coding systems by introducing a novel computational framework that leverages our recent advances in automatic verbal and nonverbal behavior analyses as well as multimodal machine learning. Our framework aims to jointly analyze verbal (i.e., what is being said), nonverbal (i.e., how something is said), and dyadic (i.e., in what interpersonal context something is said) behavior to better identify in-session patient behavior that is predictive of post-session alcohol use. The project is heavily focused on machine learning, NLP, and data mining; it requires no data collection as all data has already been collected.

We are looking to add a talented machine learning (NLP, CV, or signal processing focus) Postdoctoral Research Associate to our interdisciplinary team of machine learning scientists, affective computing experts, and psychiatrists.  Join our team's mission to better understand therapy processes and predict outcomes!

Responsibilities include:

? Design and implement state-of-the-art NLP machine learning algorithms to automatically code dyadic MI therapy sessions and predict behavior change in patients.
? Push the envelope on current NLP and multimodal machine learning algorithms to better understand the MI process and outcome.
? Conduct statistical analysis on verbal, nonverbal and dyadic behavioral patterns to describe their relationship with the MI process and outcome.
? Write and lead authorship of high impact conference (ACL, EMNLP, ICMI, CVPR, ICASSP, and Interspeech) and journal papers (PAMI, TAFFC, and TASLP).
? Support and lead graduate, undergraduate students, and summer interns to preprocess and annotate multimodal MI data.
 

Work collaboratively with:               

? Domain experts of MI research to automatically derive meaningful insights for MI research Experts.
? Computer scientists across departments at the highly accomplished and interdisciplinary USC Institute for Creative Technologies


  Have fun & learn while working at ICT with a great team and an incredible mission!


Minimum Education: PhD in computer science or engineering with a focus on NLP, CV signal processing or multimodal machine learning. 

Minimum Experience: At least 1 year of experience working with data compromising human verbal and/or nonverbal behavior. 
Minimum Field of Expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures, and analysis methods. 
Skills: Comfortable with machine learning frameworks such as PyTorch or Tensorflow Excellent programming skills in Python or C++ Analysis Assessment/evaluation Communication-written and oral skills Organization Planning Problem identification and resolution Project management Research
 
 
 
Top

6-38(2019-11-03) Ingénieur de recherche, IRIT, Toulouse France

Dans le cadre du laboratoire commun ALAIA, l'IRIT (équipe SAMoVA https://www.irit.fr/SAMOVA/site/) recrute un ingénieur de recherche en CDD pour intégrer son équipe de recherche, travailler dans le domaine de l'IA appliquée à l'apprentissage des langues étrangères et collaborer avec la société Archean Technologie (http://www.archean.tech/archean-labs-en.html).

Poste à pourvoir : Ingénieur de recherche
Durée: 12 à 18 mois
Prise de poste : possible dès le 1er décembre 2019
Domaine : traitement de la parole, machine learning, analyse automatique de la prononciation 
Lieu : Institut de Recherche en Informatique de Toulouse (Université Paul Sabatier) -  Équipe SAMoVA 
Profil recherché : titulaire d'un doctorat en informatique, machine learning, traitement de l'audio. 
Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr)  
Dossier de candidature : CV, résumé de la thèse, lettre de motivation, recommandations/contacts
Détail de l'offre :  https://www.irit.fr/SAMOVA/site/assets/files/engineer/ALAIA_ResearchEngineerPosition(1).pdf
Salaire : selon expérience 

Top

6-39(2019-11-05) Annotateur/Transcripteur H/F at ZAION, Paris, France

ZAION (https://www.zaion.ai) est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.

ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 12 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).

Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une belle aventure au sein d?une équipe dynamique qui a l?ambition de devenir la référence sur le marché des robots conversationnels.

Au sein de notre activité Intelligence Artificielle, pour appuyer ses innovations constantes concernant l'identification automatique des sentiments et émotions au sein d'interactions conversationnelles téléphoniques, nous recrutons un Annotateur/Transcripteur H/F :

 

Ses missions principales :

  • ANNOTER avec exactitude les échanges entre un client et son conseiller selon des balises expliquées sur un guide,
  • travailler avec minutie à partir de documents audio et texte en français,
  • se familiariser rapidement avec un logiciel d'annotation dédié,
  • connaître les outils de travail collaboratif,
  • utiliser ses connaissances culturelles, langagières et grammaticales pour rendre compte avec une grande précision non seulement de la conversation entre deux interlocuteurs sur un sujet donné mais aussi de la segmentation de leurs propos.

  • Le profil du candidat :
  •  être locuteur natif et avoir une orthographe irréprochable,
  • avoir une très bonne maîtrise des environnements Mac OU Windows OU Linux, - faire preuve de rigueur, d?écoute et de discrétion.

     Contrat en CDD (temps complet), basé à Paris (75017)

    Si intéressé(e), prière de contacter Anne le Gentil/RRH à l?adresse suivante : alegentil@zaion.ai en joignant au mail un C.V
Top

6-40(2019-11-05) Data Scientist /Machine Learning appliqué à l'Audio H/F, at Zaion, Paris, France

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

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

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

Nous rejoindre, c?est prendre part à une aventure passionnante et innovante afin de devenir la référence sur le marché des robots conversationnels. Dans le cadre de son développement ZAION recrute son Data Scientist /Machine Learning appliqué à l?Audio H/F. Au sein de l?équipe R&D, votre rôle est stratégique dans le développement et l?expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu?ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse?) et donc adapter leurs réponses en conséquence.

Vos missions principales :

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

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

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

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

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

- Déployez vos modèles en production

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

Qualifications requises et expérience antérieure :

-Vous avez une expérience de 2 ans minimum comme Data Scientist/Machine Learning appliqué à l?Audio

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

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

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

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

- Vous maîtrisez les techniques du traitement du signal audio

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

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

- Vous parlez anglais couramment

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

 

Très cordialement


Anne le Gentil/RRH

alegentil@zaion.ai/0662339864

 

https://www.linkedin.com/company/zaion-callbot/

Top

6-41(2019-11-25) Offre de stage, INRIA Bordeaux, France

Offre de stage M2 (Informatique/traitement du signal)



Deep Learning pour la classification entre la maladie de Parkinson et l'atrophie multisystématisée par analyse du signal vocal



La maladie de Parkinson (MP) et l'atrophie multisystématisée (AMS) sont des maladies neurodégénératives. AMS appartient au groupe des troubles parkinsoniens atypiques. 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 AMS-P et MP peut être très difficile dans les stades précoces de la maladie, tandis que la certitude de diagnostic précoce est importante pour le patient en raison du pronostic divergent. Malgré des efforts récents, aucun marqueur objectif valide n'est actuellement disponible pour guider le clinicien dans ce diagnostic différentiel. La besoin de tels marqueurs est donc très élevé dans la communauté de la neurologie, en particulier compte tenu de la gravité du pronostic AMS.

Il est établi que les troubles de la parole, communément appelés dysarthrie, sont un symptôme précoce commun aux deux maladies et d'origine différente. Nous menons ainsi des recherches qui consistent à utiliser la dysarthrie, grâce à un traitement numérique des enregistrements vocaux des patients, comme un vecteur pour distinguer entre MP et AMS-P. Nous coordonnons actuellement un projet de recherche sur cette thématique avec des partenaires cliniciens, neurologues et ORL, des CHU de Bordeaux et Toulouse. Dans le cadre de ce projet nous disposons d?une base de données d?enregistrements vocaux de patients MP et AMS-P (et de sujets saints).

 

Le but de ce stage est d?explorer des techniques récentes de Deep Leaning pour effectuer la classification entre MP et AMS-P. La première étape du stage consistera en l?implémentation d?un système baseline utilisant des outils standards et en se basant sur la méthodologie décrite dans [1]. Cette dernière traite la classification entre MP et les sujets saints et utilise des «chunks » de spectrogrammes comme entrée à un réseau neuronale convolutionnel (CNN). Cette méthodologie sera appliquée à la tâche MP vs AMS-P en utilisant notre base de données. L?implémentation du CNN se fera avec Keras-Tensorflow (https://www.tensorflow.org/guide/keras). L?extraction des paramètres du signal vocal sera effectuée par Matlab et le logiciel Praat (http://www.fon.hum.uva.nl/praat/). Cette étape permettra au stagiaire d?assimiler les briques de base du Deep Learning et de l?analyse la voix pathologique.

 

La deuxième étape de stage consistera à développer un réseau de neurones profonds (DNN) qui prend en entrée des représentations acoustiques dédiées à la tâche MP vs AMS-P et développés par notre équipe. Il s?agira de :

  • construire le bon jeu de données

  • définir la bonne classe de DNN à utiliser

  • construire la bonne architecture du DNN

  • poser la bonne fonction objective à optimiser

  • analyser et comparer les performances de classification

Cette étape nécessitera une meilleure compréhension des aspects théoriques et algorithmiques du Deep Learning.

 

Pré-requis : Une bonne connaissance des techniques standards en apprentissage statistique (Machine Learning) et de leur conceptualisation est nécessaire. Un bon niveau en programmation Python est aussi nécessaire. Des connaissances en traitement du signal/image et/ou Deep Learning seraient avantageuses. Un test sera effectué pour vérifier ces pré-requis.

 

Responsable du stage : Khalid Daoudi (khalid.daoudi@inria.fr)

Lieu du stage :Équie GeoStat (https://geostat.bordeaux.inria.fr)

INRIA Bordeaux Sud-Ouest (https://www.inria.fr/centre/bordeaux)

Durée du Stage :4 à 6 mois à partir de Février 2020

munération : Gratification standard (~580euros/mois)

 

Le (la) candidat(e) doit envoyer un CV détaillé ainsi que le nom et coordonnées d?au moins une référence à khalid.daoudi@inria.fr.

 

Le stage pourrait déboucher sur une offre de thèse.

 

[1] Convolutional neural network to model articulation impairments in patients with Parkinson?s disease

VJ. C. Vásquez-Correa, J. R. Orozco-Arroyave, and E. Nöth

in Proceedings of INTERSPEECH?2017

Top

6-42(2019-11-15) 13 PhD studentships at UKRI Centre for Doctoral Training (CDT), University of Sheffield, UK

UKRI Centre for Doctoral Training (CDT) in Speech and Language Technologies (SLT) and their Applications 

 

Department of Computer Science

Faculty of Engineering 

University of Sheffield

 

Fully-funded 4-year PhD studentships for research in Speech and Language Technologies (SLT) and their Applications

** Apply now for September 2020 intake. Up to 13 studentships available **

Deadline for applications: 31 January 2020. 

What makes the SLT CDT different:

  • Unique Doctor of Philosophy (PhD) with Integrated Postgraduate Diploma (PGDip) in SLT Leadership. 

  • Bespoke cohort-based training programme running over the entire four years providing the necessary skills for academic and industrial leadership in the field, based on elements covering core SLT skills, research software engineering (RSE), ethics, innovation, entrepreneurship, management, and societal responsibility.  

  • The centre is a world-leading hub for training scientists and engineers in SLT ? two core areas within artificial intelligence (AI) which are experiencing unprecedented growth and will continue to do so over the next decade.

  • Setting that fosters interdisciplinary approaches, innovation and engagement with real world users and awareness of the social and ethical consequences of work in this area.

 

The benefits:

  • Four-year fully-funded studentship covering all fees and an enhanced stipend (£17,000 pa)

  • Generous personal allowance for research-related travel, conference attendance, specialist equipment, etc.

  • A full-time PhD with integrated PGDip incorporating 6 months of foundational SLT training prior to starting your research project 

  • Supervision from a team of over 20 internationally leading SLT researchers, covering all core areas of modern SLT research, and a broader pool of over 50 academics in cognate disciplines with interests in SLTs and their application

  • Every PhD project underpinned by a real-world application, directly supported by one of over 30 industry partners. Partners include Google, Amazon, Microsoft, Nuance, NHS Digital and many more

  • A dedicated CDT workspace within a collaborative and inclusive research environment hosted by the Department of Computer Science

  • Work and live in Sheffield - a cultural centre on the edge of the Peak District National Park which is in the top 10 most affordable and safest UK university cities.

 

About you:

We are looking for students from a wide range of backgrounds interested in Speech and Language Technologies. 

  • High-quality (ideally first class) undergraduate or masters (ideally distinction) degree in a relevant discipline. Suitable backgrounds include (but not limited to) computer science, informatics, engineering, linguistics, speech and language processing, mathematics, cognitive science, AI, physics, or a related discipline. 

  • Regardless of background, you must be able to demonstrate mathematical aptitude (minimally to A-Level standard or equivalent) and experience of programming.

  • We particularly encourage applications from groups that are underrepresented in technology.

  • Candidates must satisfy the UKRI funding eligibility criteria. Students must have settled status in the UK and have been ?ordinarily resident? in the UK for at least 3 years prior to the start of the studentship. Full details of eligibility criteria can be found on our website.

 

Applying:

Applications are now sought for the September 2020 intake. Up to 13 studentships available.

 

We operate a staged admissions process, with application deadlines throughout the year. 

The first deadline for applications is 31 January 2020. The second deadline is 31 May 2020. 

Applications will be reviewed within 4 weeks of each deadline and short-listed applicants will be invited to interview. Interviews will be held in Sheffield.

In some cases, because of the high volume of applications we receive, we may need more time to assess your application. If this is the case, we will let you know if we intend to do this.

We may be able to consider applications received after 31 May 2020 if places are still available. Equally, all places may be allocated after the first deadline therefore we encourage you to apply early.

 

See our website for full details and guidance on how to apply: slt-cdt.ac.uk 

For an informal discussion about your application please contact us by email at: sltcdt-enquiries@sheffield.ac.uk

 

By replying to this email or contacting sltcdt-enquiries@sheffield.ac.uk you consent to being contacted by the University of Sheffield in relation to the CDT. You are free to withdraw your permission in writing at any time.

Top

6-43(2019-11-21) Bourses en études françaises (MA et PhD) à l'université Western, Canada

 


Bourses en études françaises (MA et PhD) à l'université Western

 

Le département d’études françaises de l’université Western (London, Canada) accepte maintenant les demandes d’admission pour l’année académique 2020-2021 pour ses programmes de maîtrise et de doctorat, dans les domaines de la linguistique et de la littérature. L’université Western est reconnue comme une des grandes universités de recherche en Ontario et le département d’études françaises participe activement à maintenir sa réputation depuis plus de 50 ans.

 

Le corps professoral et l’ensemble des étudiants et étudiantes participant aux programmes d’études supérieures forment une communauté internationale diversifiée. Nous offrons la possibilité de conduire un programme de recherche en linguistique formelle (syntaxe, morphologie, phonologie et sémantique) de même qu’en sociolinguistique.Nous offrons aussi une formation en littérature dans tous les siècles et tous les domaines de la littérature française et francophone, domaines dans lesquels nos étudiants et étudiantes conduisent leur recherche.

 

Vous pouvez vous renseigner quant aux champs d’intérêt du corps professoral en cliquant ici : https://www.uwo.ca/french/people/faculty/index.html.

 

Vous trouverez la liste des thèses et mémoires complétés depuis 2003 ici : https://www.uwo.ca/french/graduate/thesis/index.html.

 

Date limite pour le premier appel donnant accès au financement à partir de septembre 2020: 1er février 2020

 

Les candidatures canadiennes et internationales retenues pour le programme de doctorat reçoivent une bourse d’études d’une durée de quatre ans couvrant les frais de scolarité ainsi qu’un assistanat d’enseignement annuel d’une valeur minimale de $13 000. Le même financement est offert aux étudiants canadiens acceptés à la maîtrise pour une durée d’une année. Les étudiants internationaux acceptés au programme de maîtrise reçoivent un montant forfaitaire de $3 000 pour toute la durée du programme.

 

En plus des bourses de cycles supérieurs, le département d’études françaises offre aux étudiants et aux étudiantes qui maintiennent un dossier académique de qualité une aide financière pour effectuer des voyages de recherche ou pour prendre part à des colloques, ainsi que la possibilité de remplacer l’assistanat d’enseignement par une bourse de recherche d’une valeur équivalente. Plusieurs étudiants de notre programme de doctorat profitent aussi d’un régime de cotutelle avec une université française.

 

Pour plus d’information concernant l’aide financière offerte par notre institution, veuillez communiquer directement avec le département d’études françaises ou consultez le lien suivant :http://www.uwo.ca/french/graduate/finances/index.html .

 

Nous offrons aussi un excellent programme de formation des assistants d’enseignement de même que plusieurs activités de développement professionnel.

 

 

 

Directeur des cycles supérieures : François Poiré (fpoire@uwo.ca)

Adjointe aux cycles supérieurs : Chrisanthi Ballas (frgrpr@uwo.ca)

Pour nous joindre :http://www.uwo.ca/french/graduate/programs/index.html

 

url de référence

http://www.uwo.ca/french/graduate



Top

6-44(2019-11-22) Master R2 Internship, Loria-Inria, Nancy, France

Master R2 Internship in Natural Language Processing: weakly supervised learning for hate speech detection

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA

Contact: illina@loria.fr, dominique.fohr@loria.fr

Duration: 5-6 months

Deadline to apply : March 1th, 2020

Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

Motivations and context

Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.

Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).

In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. Developments in Neural Network led to a renewed interest in the field of distributional semantics, more specifically in learning word embeddings (representation of words in a continuous space). Computational efficiency was one big factor which popularized word embeddings. The word embeddings capture syntactic as well as semantic properties of the words (Mikolov et al., 2013). As a result, they outperformed several other word vector representations on different tasks (Baroni et al., 2014).

Our methodology in the hate speech detection is related on the recent approaches for text classification with Neural Networks and word embeddings. In this context, fully connected feed forward networks, Convolutional Neural Networks (CNN) and also Recurrent/Recursive Neural Networks (RNN)  have been applied. On the one hand, the approaches based on CNN and RNN capture rich compositional information, and have outperformed the state-of-the-art results in text classification; on the other hand they are computationally intensive and require huge corpus of training data.

To train these DNN hate speech detection systems it is necessary to have a very large corpus of training data. This training data must contains several thousands of social media comments and each comment should be labeled as hate or not hate. It is easy to automatically collect social media and Internet comments. However, it is time consuming and very costly to label huge corpus. Of course, for several hundreds of comments this work can be manually performed by human annotators. But it is not feasible to perform this work for a huge corpus of comments. In this case weakly supervised learning can be used : the idea is to train a deep neural network with a limited amount of labelled data.

The goal of this master internship is to develop a methodology to weakly supervised learning of a hate speech detection system using social network data (Twitter, YouTube, etc.).

Objectives

In our Multispeech team, we developed a baseline system for automatic hate speech detection. This system is based on fastText and BERT embeddings (Bojanowski  et al., 2017; Devlin et al, 2018) and the methodology of CNN/RNN. During this internship, the master student will work on this system in following directions:

  • Study of the state-of-the-art approaches in the field of weakly supervised learning;
  • Implementation of a baseline method of weakly supervised learning for our system;
  • Development of a new methodology for weakly supervised learning. Two cases will be studied. In the first case, we train the hate speech detection system using a small labeled corpus. Then, we proceed incrementally. We use this first system to label more data, we retrain the system and use it to label new data, In the second case, we refer to learning with noisy labels (labels that can be not correct or given by several annotators who do not agree).

References

Baroni, M., Dinu, G., and Kruszewski, G.  ?Don?t count, predict! a systematic comparison of context-counting vs. contextpredicting semantic vectors?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 238-247, 2014.

Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. ?Enriching word vectors with subword information?. Transactions of the Association for Computational Linguistics, 5:135?146, 2017.

Dai, A. M. and Le, Q. V. ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc, 2015.

Devlin J.,   Chang M.-W., Lee K., Toutanova K. ?BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding?, arXiv:1810.04805v1, 2018.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc, 2013b.

Schmidt A., Wiegand M. ?A Survey on Hate Speech Detection using Natural Language Processing?, Workshop on Natural Language Processing for Social Media, 2017.

Zhang, Z., Luo, L. ?Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter?. arxiv.org/pdf/1803.03662, 2018.

 

Top

6-45(2019-11-25) Annotateur/Transcripteur, ZAION, Paris, France

ZAION (https://www.zaion.ai) est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.

ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 12 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).

Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une belle aventure au sein d?une équipe dynamique qui a l?ambition de devenir la référence sur le marché des robots conversationnels.

Au sein de notre activité Intelligence Artificielle, pour appuyer ses innovations constantes concernant l'identification automatique des sentiments et émotions au sein d'interactions conversationnelles téléphoniques, nous recrutons un Annotateur/Transcripteur H/F :

 

Ses missions principales :

  • ANNOTER avec exactitude les échanges entre un client et son conseiller selon des balises expliquées sur un guide,
  • travailler avec minutie à partir de documents audio et texte en français,
  • se familiariser rapidement avec un logiciel d'annotation dédié,
  • connaître les outils de travail collaboratif,
  • utiliser ses connaissances culturelles, langagières et grammaticales pour rendre compte avec une grande précision non seulement de la conversation entre deux interlocuteurs sur un sujet donné mais aussi de la segmentation de leurs propos.

     

    Le profil du candidat :
  •  être locuteur natif et avoir une orthographe irréprochable,
  • avoir une très bonne maîtrise des environnements Mac OU Windows OU Linux, - faire preuve de rigueur, d?écoute et de discrétion.  

     Contrat en CDD (temps complet ou partiel), basé à Paris (75017)

     Si intéressé(e), prière de contacter Anne le Gentil/RRH à l?adresse suivante : alegentil@zaion.ai en joignant au mail un C.V
Top

6-46(2019-12-02) 2 postes d'enseignant-chercheur, Université Paris-Saclay, France

2 postes d'enseignant-chercheur (un PR et un MC) vont être mis au concours par
l'Université Paris-Saclay en section 27 lors du concours de 2020, avec des profils en
Traitement des Langues, dont la Parole en priorité et une recherche qui se fera au LIMSI.

Les deux profils sont détaillés ici :

https://www.limsi.fr/fr/limsi-emplois/offres-de-postes-chercheurs-et-enseignants-chercheurs N'hésitez pas à prendre contact si l'un des postes vous intéresse (dir@limsi.fr), et à faire savoir autour de vous l'existence de ces
postes.

Top

6-47(2019-12-03) Ph studentships, University of Glasgow, UK

The School of Computing Science at the University of Glasgow is offering studentships and excellence bursaries for PhD study. The following sources of funding are available:

 

* EPSRC DTA awards: open to UK or EU applicants who have lived in the UK for at least 3 years (see https://epsrc.ukri.org/skills/students/help/eligibility/) - covers fees and living expenses

* College of Science and Engineering Scholarship: open to all applicants (UK, EU and International) - covers fees and living expenses

* Centre for Doctoral Training in Socially Intelligent Artificial Agents: open to UK or EU applicants who have lived in the UK for at least 3 years through a national competition – see https://socialcdt.org

* China Scholarship Council Scholarship nominations: open to Chinese applicants – covers fees and living expenses

* Excellence Bursaries: full fee discount for UK/EU applicants; partial discount for international applicants

* Further scholarships (contact potential supervisor for details): open to UK or EU applicants

 

Whilst the above funding is open to students in all areas of computing science, applications in the area of Human-Computer Interaction are welcomed. 

 

Please find below a list of Available supervisors in HCI and their research areas.

 

Available supervisors and their research topics:  

* Prof Stephen Brewster (http://mig.dcs.gla.ac.uk/): Multimodal Interaction, MR/AR/VR, Haptic feedback. Email: Stephen.Brewster@glasgow.ac.uk

* Prof Matthew Chalmers (https://www.gla.ac.uk/schools/computing/staff/matthewchalmers/): mobile and ubiquitous computing, focusing on ethical systems design and healthcare applications. Email: Matthew.Chalmers@glasgow.ac.uk

* Prof Alessandro Vinciarelli (http://www.dcs.gla.ac.uk/vincia/): Social Signal Processing. Email: Alessandro.Vinciarelli@glasgow.ac.uk
* Dr Mary Ellen Foster (http://www.dcs.gla.ac.uk/~mefoster/): Social Robotics, Conversational Interaction, Natural Language Generation. Email: MaryEllen.Foster@glasgow.ac.uk
* Dr Euan Freeman (http://euanfreeman.co.uk/): Interaction Techniques, Haptics, Gestures, Pervasive Displays. Email: Euan.Freeman@glasgow.ac.uk

* Dr Fani Deligianni (http://fdeligianni.site/): Characterising uncertainty, eye-tracking, EEG, bimanual teleoperations. Email: fadelgr@gmail.com

* Dr Helen C. Purchase (http://www.dcs.gla.ac.uk/~hcp/): Visual Communication, Information Visualisation, Visual Aesthetics. Email: Helen.Purchase@glasgow.ac.uk

* Dr Mohamed Khamis (http://mkhamis.com/): Human-centered Security and Privacy, Eye Tracking and Gaze-based Interaction, Interactive Displays. Email: Mohamed.Khamis@glasgow.ac.uk

 

The closing date for applications is 31 January 2020.  For more information about how to apply, see https://www.gla.ac.uk/schools/computing/postgraduateresearch/prospectivestudents.  This web page includes information about the research proposal, which is required as part of your application.

 

Applicants are strongly encouraged to contact a potential supervisor and discuss an application before the submission deadline.

 

Top

6-48(2019-12-03) Poste de chercheur au LIMSI, Orsay, Paris, France

Le LIMSI recrute un chercheur (CC) en traitement automatique des langues et traduction
automatique (H/F). Tous les détails de l'offre sont ici:

https://emploi.cnrs.fr/Offres/CDD/UPR3251-FRAYVO-002/Default.aspx

Top

6-49(2019-12-06) Stage de fin d’études d’Ingénieur ou de Master 2, INA, Bry-sur-Marne, France

Segmentation et détection automatique

des situations conflictuelles en interview politique


Stage de fin d’études d’Ingénieur ou de Master 2 – Année académique 2019-2020

Mots clés : Machine Learning, Diarization, Humanités numériques, parole politique, expressivité

Contexte

L’Institut national de l’audiovisuel (INA) est un établissement public à caractère industriel et

commercial (EPIC), dont la mission principale consiste à archiver et valoriser la mémoire

audiovisuelle française (radio, télévision et web média). L’INA assure également des missions de

recherche scientifique, de formation et de production.

Ce stage s’inscrit le cadre du projet OOPAIP (Ontologie et outil pour l’annotation des interventions

politiques). C’est un projet transdisciplinaire porté par l’INA et le CESSP (Centre européen de

sociologie et de science politique) de l’Université Paris 1 Panthéon-Sorbonne. L’objectif est de

concevoir de nouvelles approches pour élaborer des analyses détaillées, qualitatives et

quantitatives des interventions politiques médiatisés en France. Une part du projet porte sur

l’étude de la dynamique des interactions conflictuelles dans les interviews et débats politiques, ce

qui nécessite une description fine et un large corpus afin de généraliser les modèles. Les verrous

technologiques concernent la performance des algorithmes de segmentation en locuteurs et en

styles de parole. L’amélioration de leur précision, l’ajout de la détection de parole superposée, de

mesures de l’effort vocal et d’éléments expressifs, permettront d’optimiser le travail d’annotation

manuel.

Objectifs du stage

Le stage vise principalement à l’amélioration de la segmentation automatique d’interviews

politiques pour assister les travaux de recherche en science politique. La thématique de recherche

correspondante que nous retiendrons est la mise en évidence des situations conflictuelles. Dans ce

cadre, nous nous intéresserons notamment à la détection du brouhaha (parole superposée). De

manière plus fine, nous aimerions pouvoir extraire des descripteurs du signal de parole corrélés au

niveau de conflictualité des échanges, basés, par exemple, sur le niveau d’activation (niveau

intermédiaire entre le signal et l’expressivité [Rilliard et al, 2018]) ou l’effort vocal [Liénard, 2019].

Le stage pourra s’appuyer initialement sur deux corpus totalisant 30 interviews politiques annotés

finement en tours de paroles — dans le cadre du projet OOPAIP. Il débutera par la réalisation d’un

état de l’art de la diarization (segmentation et regroupement en locuteurs [Broux et al., 2019]) et

de la détection de la parole superposée [Chowdhury et al, 2019]. Il s’agira ensuite de proposer des

solutions basées sur des frameworks récents pour améliorer la localisation des frontières de tours

de parole, notamment lorsque la fréquence des changements de locuteurs est importante — le

cas limite étant la situation du brouhaha.

La seconde partie du stage se penchera sur une mesure plus fine du niveau conflictuel des

échanges, via la recherche des descripteurs les plus pertinents et par la mise au point

d’architecture d’apprentissage pour sa modélisation.

Le langage de programmation utilisé dans le cadre de ce stage sera Python. Le stagiaire aura accès

aux ressources de calcul de l’INA (serveurs et clusters), ainsi qu’à un desktop performant avec 2

GPU de génération récente.

Valorisation du stage

Différentes stratégies de valorisation des travaux du·de la stagiaire seront envisagées, en fonction

du degré de maturité des travaux réalisés :

Diffusion des outils d’analyse réalisés sous licence open-source via le dépôt GitHub de

l’INA : https://github.com/ina-foss

Rédaction de publications scientifiques

Conditions du stage

Le stage se déroulera sur une période de 4 à 6 mois, au sein du service de la Recherche de l’Ina. Il

aura lieu sur le site Bry 2, situé au 18 Avenue des frères Lumière, 94360 Bry-sur-Marne. La·le

stagiaire sera encadré·e par Marc Evrard (mevrard@ina.fr).

Gratification : environ 550 Euros par mois.

Profil recherché

Étudiant·e en dernière année d’un bac +5 dans le domaine de l’informatique et de l'IA.

Compétence en langage Python et expérience dans l’utilisation de bibliothèques de ML

(Scikit-learn, TensorFlow, PyTorch).

Vif intérêt dans les SHS, les humanités numériques et les sciences politiques en particulier.

Capacité à réaliser une étude bibliographique à partir d’articles scientifiques rédigés en

anglais.

Bibliographie

Broux, P. A., Desnous, F., Larcher, A., Petitrenaud, S., Carrive, J., & Meignier, S. (2018). “S4D: Speaker Diarization

Toolkit in Python”. In Inter-speech 2018.

Chowdhury, S. A., Stepanov, E. A., Danieli, M., Riccardi, G. (2019). “Automatic classification of speech overlaps:

Feature representation and algo-rithms”, Computer Speech & Language, vol. 55, pp.145-167.

Liénard, J.-S. “Quantifying vocal effort from the shape of the one-third octave long-term-average spectrum of speech”

J. Acoust. Soc. Am. 146 (4), Oc-tober 2019.

Rilliard, A., d’Alessandro, C & Evrard, M. (2018). Paradigmatic variation of vowels in expressive speech: Acoustic

description and dimensional analysis. The Journal of the Acoustical Society of America, 143(1), 109–122.

Top

6-50(2019-12-07) Stage à l'IRCAM, Paris, France

Deep Disentanglement of Speaker Identity and Phonetic Content for Voice

Conversion

Dates : 01/02/2020 au 30/06/2020

Laboratoire : STMS Lab (IRCAM / CNRS / Sorbonne Université)

Lieu : IRCAM – Analyse et Synthèse des Sons

Responsables : Nicolas Obin, Axel Roebel

Contact : Nicolas.Obin@ircam.fr, Axel.Roebel@ircam.fr

Contexte :

La conversion de l’identité de la voix consiste à modifier les caractéristiques d’une voix

« source » pour reproduire les caractéristiques d’une voix « cible » à imiter, à partir

d’une collection d’exemples de la voix « cible ». La tâche de conversion d’identité de la

voix s’est largement popularisée ces dernières années avec l’apparition des « deep

fakes », avec comme objectif de transposer les réussites réalisées dans le domaine de

l’image au domaine de la parole. Ainsi, les lignes de recherche actuelles reposent sur des

architectures neuronales comme les modèles séquence-à-séquence, les réseaux

antagonistes génératifs (GAN, [Goodfellow et al., 2014]) et ses variantes pour

l’apprentissage à partir de données non appareillées (Cycle-GAN [Kaneko and

Kamaeoka, 2017] ou AttGAN [He et al., 2019]). Les challenges majeurs de la conversion

d’identité comprennent la possibilité d’apprendre des transformation d’identité

efficacement à partir de petites bases de données (qq minutes) et de séparer les

facteurs de variabilité de la parole afin de modifier uniquement l’identité d’un locuteur

sans modifier ou dégrader le contenu linguistique et expressif de la voix.

Objectifs :

Le travail effectué dans ce stage concernera l’extension du système de conversion

neuronal de l’identité vocale actuellement développée dans le cadre du projet ANR

TheVoice (https://www.ircam.fr/projects/pages/thevoice/). Le focus principal du

stage sera d’intégrer efficacement l’information du contenu linguistique au système de

conversion neuronal existant. Cet objectif passera par la réalisation des tâches

suivantes :

- Développement d’une représentation de l’information phonétique (par ex. sous

forme de Phonetic PosteriorGrams [Sun et al., 2016]) et intégration au système de

conversion actuel.

- Application et approfondissement de techniques de « disentanglement » de l’identité

du locuteur et du contenu phonétique pour l’apprentissage de la conversion

[Mathieu et al., 2016 ; Hamidreza et al., 2019]

- Evaluation des résultats obtenus par comparaison à des systèmes de conversion de

l’état de l’art, sur des bases de référence comme VCC2018 ou LibriSpeech.

Les problèmes abordés pendant le stage seront sélectionnés en début du stage après une

phase d’orientation et une étude bibliographique. Les solutions réalisées au cours du

stage seront intégrées au système de conversion d’identité de la voix de l’Ircam, avec

possibilité d’exploitation industrielle et professionnelle. Par exemple, le système de

conversion d’identité développé à l’Ircam a été exploité dans des projets de production

professionnelle pour recréer des voix de personnalités historiques : le maréchal Pétain

dans le documentaire « Juger Pétain » en 2012, et Louis de Funès dans le film « Pourquoi

j’ai pas mangé mon père » de Jamel Debbouze en 2015.

Le stage s’appuiera sur les connaissances de l’équipe Analyse et Synthèse des Sons du

laboratoire STMS (IRCAM/CNRS/Sorbonne Université) en traitement du signal de parole

et en apprentissage de réseaux de neurones, et sur une grande expérience en

conversion d’identité de la voix [Villavicencio et al., 2009 ; Huber, 2015].

Compétences attendues :

- Maîtrise de l’apprentissage automatique, en particulier de l’apprentissage par

réseaux de neurones ;

- Maîtrise du traitement du signal audio numérique (analyse temps-fréquence, analyse

paramétrique de signaux audio, etc…) ;

- Bonne maîtrise de la programmation Python et de l’environnement TensorFlow ;

- Autonomie, travail en équipe, productivité, rigueur et méthodologie.

Rémunération :

Gratification selon loi en vigueur et avantages sociaux

Date limite de candidature :

20/12/2019

Bibliographie :

[Goodfellow et al., 2014] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David

Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative Adversarial

Networks,” arXiv:1406.2661 [cs, stat], 2014.

[Hamidreza et al., 2019] Seyed Hamidreza Mohammadi, Taehwan Kim. One-shot Voice

Conversion with Disentangled Representations by Leveraging Phonetic Posteriorgrams,

Interspeech 2019.

[He et al., 2019] Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, “Attgan: Facial attribute editing by

only changing what you want.,” IEEE Transactions on Image Processing, vol. 28, no. 11, 2019.

[Huber 2015] S. Huber, “Voice Conversion by modelling and transformation of extended voice

characteristics”, Thèse Université Pierre et Marie Curie (Paris VI), 2015.

[Kanekoa and Kameoka, 2017] TakuhiroKanekoandHirokazuKameoka,“Parallel-Data-Free Voice

Conversion Using Cycle-Consistent Adversarial Net- works,” arXiv:1711.11293 [cs, eess, stat],

2017

[Mathieu et al., 2016] Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann

LeCun. Disentangling factors of variation in deep representations using adversarial training,

NIPS 2016.

[Sun et al., 2016 ]Lifa Sun, Kun Li, Hao Wang, Shiyin Kang, and Helen Meng, “Phonetic

posteriorgrams for many-to-one voice conversion without parallel data training,” in 2016 IEEE

International Conference on Multimedia and Expo (ICME), 2016, pp. 1–6.

[Villavicencio et al., 2009] Villavicencio, F., Röbel, A., and Rodet, X. (2009). Applying improved

spectral modelling for high quality voice conversion. In Proc. of IEEE International Conference on

Acoustics, Speech, and Signal Processing (ICASSP), pages 4285–4288. 17, 41, 45

Top

6-51(2019-12-07) Assistant-e ingénieur-e en production, LPL, Aix en Provence, France

Emploi-type :

Assistant-e ingénieur-e en production, traitement de données et enquêtes BAP D (Donnée en SHS) :

Mission :

Au sein de plateforme expérimentale du Laboratoire Parole et Langage (LPL), l'agent sera chargé de la coordination technique, de l'accueil et du soutien aux expériences en collaboration avec les responsables de secteur (audio-vidéo, articulographie/physiologie, neurophysiologie/eye-tracking).

Activités :

Accueillir et recueillir des informations personnelles relatives aux participants dans le respect de la législation en vigueur (RGPD)
Assurer le recrutement des participants aux expériences
Interfacer avec les chercheurs extérieurs
Suivre et renouveler les consommables
Assurer la réservation des espaces expérimentaux et des matériels, établissement du planning de passation, prises de rendez-vous
Soutenir la mise en place du dispositif expérimental en lien avec le responsable de secteur
Renseignement des cahiers de laboratoire
Assurer les Campagnes permanentes pour la recherche de volontaires
Participer à la rédaction de notices méthodologiques des opérations réalisées
Actualiser ses connaissances disciplinaires et méthodologiques et répertorier la bibliographie consacrée à un champ d'études

Compétences :

Maîtrise des techniques, méthodes, et protocoles expérimentaux en SHS.
Connaissance dans le domaine de la mesure et des statistiques
Travail en collaboration avec les chercheurs dans la conception, la mise en place et la réalisation des expériences
Travail en équipe avec les autres personnels ITA intervenant sur la plateforme.
Sens aigu des relations humaines dans ses interactions avec des investigateurs aux compétences variées (des étudiants de master aux chercheurs étrangers en passant par les chercheurs et doctorants du laboratoire) et avec toutes les catégories de participants, des enfants d'âge scolaire aux adultes et personnes âgées, et dont certains peuvent présenter différentes pathologies.
Connaissance et respect de la législation dans le domaine des recherches sur la personne humaine ainsi que les règles en matière d'hygiène et de sécurité.
Bonne maîtrise de l'anglais parlé (Niveau B2 selon le cadre européen de référence pour les langues) se montrera indispensable
Archivage pérenne de données de recherche (notion)

 

La campagne est ouverte jusqu'au 17 janvier mais l'examen des candidatures se fera au fil de l'eau. N'hésitez pas à diffuser cette information auprès des personnes potentiellement concernées.

 

 

 

 

Top

6-52(2019-12-07) 1 year post-doc/engineer position at LIA, Avignon France

 1 year post-doc/engineer position at LIA, Avignon France, in the Vocal Interaction Group

Multimodal man-robot interface for social spaces

keywords: AI, ML, DNN, RL, NLP, dialogue, vision, robotics

Starting job date (desired): March 2020.
==================================================================
## Work description

###Project Summary

Automation and optimisation of *verbal interactions of a socially-competent robot*,
guided by its *multimodal perceptions*

Facing a steady increase in the ageing population and the prevalence of chronic diseases,
social robots are promising tools to include in the health care system. Yet extant
assistive robots are not well suited to such context as their communication abilities
cannot handle social spaces (several meters and group of persons) but rather face-to-face
individual interactions in quiet environments. In order to overcome these limitations and
eventually aiming at natural man-robot interaction, the objectives of the work will be
multifold.

First and foremost we intend to leverage the rich information available with audio and
visual flows of data coming from humans to extract verbal and non-verbal features. These
features will be used to enhance the robot's decision-making ability such that it can
smoothly take speech turns and switch from interaction with a group of people to
face-to-face dialogue and back. Secondly online and continual learning of the advanced
system will be investigated.

Outcomes of the project will be implemented onto a commercially available social robot
(most likely a Pepper) and validated with several in-situ use cases. A large-scale data
collection will complement in-situ tests to fuel further researches. Essential
competencies to address our overall objectives lie in dialogue systems / NLP, yet
knowledges in vision and robotics would also be necessary. And in any case good command
of deep learning techniques and tools is mandatory (including reinforcement learning for
dialogue strategy training).

### Requirements

- Master or PhD in Computer Science, Machine Learning, Computational Linguistics,
Mathematics, Engineering or related fields
- Expertise in NLP / Dialog systems. Strong knowledge of current NLP / Interactive /
Speech techniques is expected. Previous experience with dialogue and interaction and/or
vision data is a strong plus.
- Knowledge in Vision and/or Robotics are plusses.
? Strong programming skills, Python/C++ programmer of DNN models (preferably with pytorch)
- Expertise in Unix environments
- Good spoken and written command of English is required. *French is optional.*
- Good writing skills, as evidenced through publications at top venues (e.g., ACL, EMNLP,
SigDial etc) is a plus, for post-doc.

## Place

Bordered by the left bank of the Rhône Avignon is one of the most beautiful city in
Provence, for some time capital of Christendom in the Middle Ages. The important remains
of a past rich in history give the city its unique atmosphere: dozens of churches and
chapels, the ?Palais des Papes? (palace of the popes) the most important gothic palace in
Europe), the Saint-Benezet brigde, called the « pont d?Avignon » of worldwide fame
through its commemoration by the song, and the ramparts that still encircle the entire
city, ten museums from then ancient times to contemporary art.

The 94,787 inhabitants of the city, about 12,000 live in the ancient town centre
surrounded by its medieval ramparts. Avignon is not only the birthplace of the most
prestigious festival of contemporary theatre, European Capital of Culture in 2000, but
also the largest city and capital of the département of Vaucluse. The region offers a
high quality of urban life at comparatively still modest costs. In addition to this, the
region of Avignon also offers the opportunity to visit numerous monuments and natural
beauty sites easily accessible in a very short time: Avignon is the ideal destination for
visiting Provence.

LIA is the computer science lab of Avignon University: http://lia.univ-avignon.fr.

## Conditions

Net monthly salary: 1500-2100 ? (depending on the candidate's experience). Basic
healthcare coverage included (https://en.wikipedia.org/wiki/Health_care_in_France).

The position carries no direct teaching load, but if desired, teaching BSc or MSc level
courses is a possibility (paid extra hours), as is supervision of student dissertation
projects.

Initial employment is 12 months, extension is possible. For engineer, shift to a PhD
position is possible.

## Applications

No deadline: applications are possible until the position is filled.

To apply, send the following documents *as a single PDF* to
fabrice.lefevre@univ-avignon.fr:

* Statement of research interests that motivates your application
* CV, including the list of publications if any
* Scans of transcripts and academic degree certificates
* MSc/PhD dissertation and/or any other writing samples
* Coding samples or links to your contributions to public code repositories, if any
* Names, affiliations, and contact details of up to three people who can provide
reference letters for you

Delete | Reply | Reply to List | Forward | Redirect | View Thread | Blacklist | Whitelist | Message Source | Save as | Print
M
Top

6-53(2019-12-05) ​Postdoctoral Fellowship,University of Connecticut Health,Farmington, CT, USA

Postdoctoral Fellowship, Speech Processing in Noise

University of Connecticut Health

Location: Farmington, CT

Start Date: January 2020, or thereafter

Duration: Initially 1 year with potential for extension

Salary: Depends on experience, based on NIH range: benefits include health care, retirement

contributions, and paid leave for vacation, personal days, holidays and sickness.

Application Process: Please send your resumé, a one-page cover letter that describes your

research interests and experience, a list of publications (copies of most relevant - optional), and

contact information for three references to Dr Insoo Kim (ikim@uchc.edu).

A Postdoctoral Fellowship is available in the Division of Occupational Medicine, Department of

Medicine, at the University of Connecticut Health to investigate algorithms for improving speech

intelligibility in environmental noise. The work will involve simulating the noise of machines from

known frequency spectra and creating speech-in-noise test files using MATLAB for replaying to

subjects in listening tests. The test files may be processed electronically to improve intelligibility

before the psychoacoustic testing. The position requires knowledge of, and practical

experience with, speech or audio digital signal processing; proficiency with MATLAB and

Simulink simulations, and; familiarity with psychoacoustic testing of speech intelligibility in noise,

and with the development of embedded systems or digital signal processors.

The Fellow will participate in on-going research projects involving speech processing. He/she

will be responsible for implementing the algorithms for improving speech communication in

noise, conducting all psychoacoustic tests used to establish proof-of-concept, and data analysis

and interpretation. The Fellow will also have opportunities to supervise graduate and

undergraduate students.

Candidates should have good oral and written English communication skills, be capable of

independent work as a part of a multi-disciplinary team, be able to work on multiple projects at

the same time, publish results in academic journals and participate in grant proposal

preparation. They should have a Ph.D. degree in Acoustics, Electrical, Computer, Biomedical

Engineering, or a related field with appropriate experience. The initial appointment is for a

period of one year with potential for further extension. The review of applications will start

immediately and will continue until the position is filled.

Top

6-54(2019-12-08) PhD sudentship, Utrecht University, The Netherlands

The Social and Affective Computing group at the Utrecht University Department of Information and Computing Sciences is looking for a PhD candidate to conduct research on explainable and accountable affective computing for mental healthcare scenarios. The five-year position includes 70% research time and 30% teaching time. The post presents an excellent opportunity to develop an academic profile as a competent researcher and able teacher.

Affective computing has great potential for clinician support systems, but it needs to produce insightful, explainable, and accountable results. Cross-corpus and cross-task generalization of approaches, as well as efficient and effective ways of leveraging multimodality are some of the main challenges in the field. Furthermore, data are scarce, and class-imbalance is expected. While addressing these issues, precision needs to be complemented by interpretability. Potential investigation areas include for example depression, bipolar disorder, and dementia.

The PhD candidate is expected to bridge the research efforts in cross-corpus, cross-task multimodal affect recognition with explainable/accountable machine learning for the aim of efficient, effective and interpretable predictions on a data-scarce and sensitive target problem. The candidate is also expected to be involved in teaching activities within the department of Information and Computing Sciences. Teaching activities may include supporting senior teaching staff, conducting tutorials, and supervising student projects and theses. These activities will contribute to the development of the candidate's didactic skills.

We are looking for candidates with:

  • a Master?s degree in computer science/engineering, mathematics, and/or fields related to the project focus;
  • interest or experience with processing of audio/acoustics, vision/video or natural language;
  • interest or experience with machine learning, affective computing, information fusion, multimodal interaction;
  • demonstrable coding skills in high-level scripting languages such as MATLAB, python or R;
  • excellent English oral and writing skills.

The ideal candidate should express a strong interest in research in affective computing and teaching within the ICS department. The Department finds gender balance specifically and diversity in a broader sense very important; therefore women are especially encouraged to apply. Applicants are encouraged to mention any personal circumstances that need to be taken into account in their evaluation, for example parental leave or military service.

 

We offer an exciting opportunity to contribute to an ambitious and international education programme with highly motivated students and to conduct your own research project at a renowned research university. You will receive appropriate training, personal supervision, and guidance for both your research and teaching tasks, which will provide an excellent start to an academic career.

The candidate is offered a position for five years (1.0 FTE). The gross salary starts at ?2,325 and increases to ?2,972 (scale P according to the Collective Labour Agreement Dutch Universities) per month for a full-time employment. Salaries are supplemented with a holiday bonus of 8% and a year-end bonus of 8.3% per year. In addition, Utrecht University offers excellent secondary conditions, including an attractive retirement scheme, (partly paid) parental leave and flexible employment conditions (multiple choice model). More information about working at Utrecht University can be found here.

Application deadline is 01.01.2020.

 Further information and application procedure can be found here.
 
 

Top

6-55(2019-12-09) Postdoc , IRISA, Rennes, France
IRISA (France) is looking for a 30-month postdoctoral researcher for topic of Natural Language Processing for Kids, starting in Spring 2020.

 

Top

6-56(2019-12-15) PhD grant at the University of Glasgow, Scotland, UK

The School of Computing Science at the University of Glasgow is offering studentships and excellence bursaries for PhD study. The following sources of funding are available:

* EPSRC DTA awards: open to UK or EU applicants who have lived in the UK for at least 3 years (see https://epsrc.ukri.org/skills/students/help/eligibility/) - covers fees and living expenses
* College of Science and Engineering Scholarship: open to all applicants (UK, EU and International) - covers fees and living expenses
* Centre for Doctoral Training in Socially Intelligent Artificial Agents: open to UK or EU applicants who have lived in the UK for at least 3 years through a national competition – see https://socialcdt.org
* China Scholarship Council Scholarship nominations: open to Chinese applicants – covers fees and living expenses
* Excellence Bursaries: full fee discount for UK/EU applicants; partial discount for international applicants
* Further scholarships (contact potential supervisor for details): open to UK or EU applicants

Whilst the above funding is open to students in all areas of computing science, applications in the area of Human-Computer Interaction are welcomed. 

Please find below a list of Available supervisors in HCI and their research areas.

Available supervisors and their research topics:  

* Prof Stephen Brewster (http://mig.dcs.gla.ac.uk/): Multimodal Interaction, MR/AR/VR, Haptic feedback. Email: Stephen.Brewster@glasgow.ac.uk
* Prof Matthew Chalmers (https://www.gla.ac.uk/schools/computing/staff/matthewchalmers/): mobile and ubiquitous computing, focusing on ethical systems design and healthcare applications. Email: Matthew.Chalmers@glasgow.ac.uk
* Prof Alessandro Vinciarelli (http://www.dcs.gla.ac.uk/vincia/): Social Signal Processing. Email: Alessandro.Vinciarelli@glasgow.ac.uk
* Dr Mary Ellen Foster (http://www.dcs.gla.ac.uk/~mefoster/): Social Robotics, Conversational Interaction, Natural Language Generation. Email: MaryEllen.Foster@glasgow.ac.uk
* Dr Euan Freeman (http://euanfreeman.co.uk/): Interaction Techniques, Haptics, Gestures, Pervasive Displays. Email: Euan.Freeman@glasgow.ac.uk
* Dr Fani Deligianni (http://fdeligianni.site/): Characterising uncertainty, eye-tracking, EEG, bimanual teleoperations. Email: fadelgr@gmail.com
* Dr Helen C. Purchase (http://www.dcs.gla.ac.uk/~hcp/): Visual Communication, Information Visualisation, Visual Aesthetics. Email: Helen.Purchase@glasgow.ac.uk
* Dr John Williamson (https://www.johnhw.com/): Probabilistic user interfaces, Bayesian interaction, motion correlation interfaces, rich and robust human sensing systems. Email: johnh.williamson@glasgow.ac.uk
* Dr Mohamed Khamis (http://mkhamis.com/): Human-centered Security and Privacy, Eye Tracking and Gaze-based Interaction, Interactive Displays. Email: Mohamed.Khamis@glasgow.ac.uk

The closing date for applications is 31 January 2020.  For more information about how to apply, see https://www.gla.ac.uk/schools/computing/postgraduateresearch/prospectivestudents.  This web page includes information about the research proposal, which is required as part of your application.

Applicants are strongly encouraged to contact a potential supervisor and discuss an application before the submission deadline.

Best regards,
Mohamed Khamis

--
Dr. Mohamed Khamis
Lecturer of Human-centered Security
School of Computing Science
University of Glasgow
Glasgow, G12 8RZ, UK

Tel +44 (0) 141 330 8078
Mohamed.Khamis@glasgow.ac.uk
https://www.gla.ac.uk/schools/computing/staff/mohamedkhamis/
http://mkhamis.com/

Top

6-57(2019-12-19) Postdoc at Bielefeld University, Germany

The Faculty of Linguistics and Literary Studies at Bielefeld University offers a full-time

 

research position (postdoctoral position, E13 TV-L, non-permanent) in phonetics

 

The Faculty of Linguistics and Literary Studies offers a full time post-doctoral position in phonetics for 3 years (German pay scale: E13). 

The Bielefeld phonetics group is well known for its research on phenomena in spontaneous interaction, prosody, multimodal speech and spoken human-machine interaction. Bielefeld campus offers a wide range of options for intra and interdisciplinary networking, and further qualification.

 

Your responsibilities:

- conduct independent research in phonetics, with a visible focus on modeling or speech technology (65%).

- teach 2 classes (3 hours=4 teaching units/week) per semester in the degree  offered by the linguistics department, including the supervision of BA and MA theses and conducting exams (25%).

- organizational tasks that are part of the self-administration of the university (10%).

 

Necessary qualifications:

 

- a Masters degree in a relevant discipline (e.g., phonetics, linguistics, computer science, computational linguistics)

- a doctoral degree in a relevant discipline

- a research focus in phonetics or speech technology

- state-of-the-art knowledge in statistical methods or programming skills

- knowledge in generating and analyzing speech data with state-of-the-art tools

- publications

- teaching experience

- a co-operative and team oriented attitude

- an interest in spontaneous, interactive, potentially multimodal data

 

Preferable qualifications:

 

- experience in the acquisition of third party funding

 

Remuneration

 

Salary will be paid according to Remuneration level 13 of the Wage Agreement for Public Service in the Federal States (TV-L). As stipulated in § 2 (1) sentence 1 of the WissZeitVG (fixed-term employment), the contract will end after three years, In accordance with the provisions of the WissZeitVG and the Agreement on Satisfactory Conditions of Employment, the length of contract may differ in individual cases. The employment is designed to encourage further academic qualification. In principle, these full-time position may be changed into a part-time position, as long as this does not conflict with official needs.

Bielefeld University is particularly committed to equal opportunities and the career development of its employees. It offers attractive internal and external training and further training programmes. Employees have the opportunity to use a variety of health, counselling, and prevention programmes. Bielefeld University places great importance on a work-family balance for all its employees.

Application Procedure

For full consideration, your application should be received via either post (see postal address below) or email (a single PDF) document sent to alexandra.kenter@uni-bielefeld.de by January 8th, 2020. Please mark your application with the identification code: wiss19299. To apply, please provide the usual documents (CV including information about your academic education and degrees, professional experience, publications, conference contributions, and further relevant skills and abilities). The application can be written in German or English.

Further information on Bielefeld University can be found on our homepage at www.uni-bielefeld.de. Please note that the possibility of privacy breaches and unauthorized access by third parties cannot be excluded when communicating via unencrypted e-mail. Information on the processing of personal data is available https://www.uni-bielefeld.de/Universitaet/Aktuelles/Stellenausschreibungen/2019_DS-Hinweise_englisch.pdf.

Postal Address

Bielefeld University, Faculty of Linguistics and Literary Studies, Prof. Dr. Petra Wagner, P.O. Box: 10 01 31, 33501 Bielefeld, Germany

Contact

Alexandra Kenter

0521 106-3662

alexandra.kenter@uni-bielefeld.de

Top

6-58(2019-12-22) Postdoctoral Researcher, University of Toulouse Jean Jaures, France

Postdoctoral Researcher ? Psycholinguistics, neurolinguistics, corpus linguistics, clinical linguistics
Full-Time Position, Fixed-term 1 year (with possibility of one year extension)

Application deadline: 05/01/2020
Starting date : 01/02/2020 (flexible)

The Octogone-Lordat Lab (University of Toulouse Jean Jaurès, France : https://octogone.univ-tlse2.fr/) offers a post-doctoral position for 1 year, with a possibility of 1 year extension.

The neuropsycholinguistic study of language processing is the major topic of our lab, focusing on typical language use, language disorders and rehabilitation processes (as in aphasia), at the intersection of linguistics, psycholinguistics and neuroscience.

The post-doc will contribute to the project ?Aphasia, Discourse Analysis and Interactions? funded by the European Regional Development Fund and the Région Occitanie - France. Strong background in linguistics, psycholinguistics or neurolinguistics, cognitive science, and in methodological skills for data collection in corpus linguistics and clinical linguistics as well are required. The post-doc will actively contribute to the development of a new database focusing on typical and atypical language in aphasia. Along with the project supervisors, the post-doc will be involved in all activities in line with the project (e. g. IRB approval, GDPR conformity, etc.), including data collection, coding and analyses from various perspectives. Attested experience with empirical and experimental methods (corpus linguistics) is appreciated, as well as a strong research interest for clinical issues. The post-doc will also coordinate trainees? and students? work involved in the project, and contribute significantly to publication of the findings. The applicant should, at least, have completed a PhD in Linguistics, Neuropsychology, Cognitive Science or related fields, and prove high proficiency level in French according to the CEFRL. Good skills in spoken and written academic English are also required.

This is a full time position starting in February 2020 (flexible).
Gross annual salary : min. ?28,000 to ?32,000 (before 15% to 25% taxes and social-security deduction, INM 528 to 564 in accordance with public sector pay scale)

The application should include a CV, a statement of motivations, a link to the PhD thesis, PhD Viva report (if available), plus 2 scanned letters of recommendation.

Deadline for application :  05/01/2020, to :
Dr. Halima Sahraoui, sahraoui@univ-tlse2.fr
Prof. Barbara Köpke, bkopke@univ-tlse2.fr

For further questions and application submission, please feel free to contact us.

Octogone-Lordat (EA 4156)
https://octogone.univ-tlse2.fr/
Université de Toulouse 2
Maison de la Recherche ? E126
5, Allées Antonio Machado
31058 Toulouse Cedex 9
France

About city life in Toulouse :
https://www.toulouse-visit.com/

Top

6-59(2019-12-24) Postdoc proposal, Grenoble, France

Postdoc proposal

Lexicon Free Spontaneous Speech Recognition

using Sequence-to-Sequence Models.

December 20, 2019

1 Postdoc Subject

The goal of the project is to advance the state-of-the-art in spontaneous auto-

matic speech recognition (ASR). Recent advances in ASR show excellent per-

formances on tasks such as read speech ASR (Librispeech), TV shows (MGB

challenge), but what about spontaneous communicative speech ?

This postdoc project would leverage existing transcribed corpora (more than

300 hours) recorded in everyday communication (speech recordings inside a

family, in a shop, during an interview, etc.).

We will investigate lexicon free methods based on sequence-to-sequence ar-

chitectures and analyze the representations learnt by the models.

Research topics:

 End-to-end ASR models

 Spontaneous speech ASR

 Data augmentation for spontaneous language modelling

 Use of contextualized language models (such as BERT) for ASR re-scoring

 Analyzing representations learnt by ASR systems

2 Requirements

We are looking for an outstanding and highly motivated postdoc candidate to

work on this subject. Following requirements are mandatory:

 PhD degree in natural language processing or speech processing.

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

works).

1

 Interest in speech technology and speech science

 Good oral and written communication in English (French is a plus while

not mandatory)

 Ability to work autonomously and in collaboration with other team mem-

bers and other disciplines

3 Work context

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

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

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

Institute (MIAI). The candidate will work both at LIG-lab (GETALP team)

and LIDILEM-lab. The duration of the postdoc is 18 months.

4 How to apply?

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

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

one-page summary of the PhD thesis. Applications should be sent to lau-

rent.besacier@imag.fr, solange.rossato@imag.fr and aurelie.nardy@univ-grenoble-

alpes.fr. Applications will be evaluated as they are received: the position is open

until it is lled.

Top

6-60(2019-12-30) Internship at LIG, Grenoble, France

Neural coreference resolution

Coreference resolution aims at detecting chains of coreference mentions in a text, that is mentions in the text that refer to the same entity.

While at first coreference resolution was split into two separated sub-problems, i.e. mention detections and resolution of coreferent mentions [1], thanks to the development of sophisticated neural models [2,3,4], end-to-end coreference resolution system can be based on a whole single model.
The aim of this stage is to study Sequence-to-Sequence [5] and Transformer [6] neural models for coreference resolution, integrating different types of attention mechanisms and possibly arbitrarily-long context [8], with the goal of understanding their impact in dealing with this complex NLP problem.

In this internship the student will implement parts of the systems for coreference resolution with Sequence-to-Sequence and Transformer neural models.
The student will run experiments on his own using GPUs, and the systems will be tested on the CoNLL Semeval 2012 benchmark [7].

Profile:
- Student for internship level stage (Master 2) in computer science, or from engineering school

- Computer science skills:
    Python programming with good knowledge of deep learning libraries (TensorFlow or PyTorch)
    Textual data manipulation (xml format, tabular format, CoNLL format)
- Interested in Natural Language Processing
- Skills in machine learning for probabilistic models

Context:

The internship may last from 4 up to 6 months, it will take place at LIG laboratory, GETALP team (http://lig-getalp.imag.fr/), starting from January/February 2019.
The student will be tutored by Marco Dinarelli (http://www.marcodinarelli.it) and Laurent Besacier (https://cv.archives-ouvertes.fr/laurent-besacier).
Interested candidates must send a CV and a motivation letter to marco.dinarelli@ens.fr and laurent.besacier@univ-grenoble-alpes.fr.

References:

[1]  Vincent Ng

    Supervised noun phrase coreference research: The first fifteen years.
    Proceedings of ACL, 2010

[2] Sam Wiseman, Alexander M. Rush, Stuart M. Shieber
    Learning Global Features for Coreference Resolution
    Proceedings of NAACL-HLT, 2016

[3] Kenton Leey, Luheng Hey, Mike Lewisz, and Luke Zettlemoyer
    End-to-end Neural Coreference Resolution
    Proceedings of EMNLP, 2017

[4] Kenton Lee Luheng He Luke Zettlemoyer
    Higher-order Coreference Resolution with Coarse-to-fine Inference
    Proceedings of NAACL, 2018

[5] Ilya Sutskever, Oriol Vinyals, Quoc V. Le
    Sequence to Sequence Learning with Neural Networks
    Proceedings of NIPS, 2014

[6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
    Attention Is All You Need
    Proceedings of NIPS, 2017

[7]  Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, Yuchen Zhang
     Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes
    Proceedings of EMNLP and  CoNLL-Shared Task, 2012

[8] Zhang, Jiacheng, et al. 'Improving the Transformer Translation Model with Document-Level Context.? EMNLP 2018.
 
Top

6-61(2020-01-09) 12-month Postdoctoral research position at GIPSA Lab, Grenoble, France

12-month Postdoctoral research position in

machine learning for neural speech decoding

Place: GIPSA-lab (CNRS/UGA/Grenoble-INP) in collaboration with BrainTech laboratory (INSERM). Both

laboratories are located on the same campus in Grenoble, France.

Team: CRISSP team@GIPSA-lab (Cognitive Robotics, Interactive System and Speech processing).

Context:

This position is part of the ANR (French National Research Agency) BrainSpeak project aiming at developing a

Brain-Computer Interface (BCI) for speech rehabilitation, based on large-scale neural recordings. This post-doc

position aims at developing new machine learning algorithms to improve the conversion of neural signals into an

intelligible acoustic speech signal.

Mission:

Investigate deep learning approaches to map intracranial recordings (ECoG) to speech features (spectral,

articulatory, or linguistic features). A particular focus will be put on 1) weakly or self-supervised training in order to

deal with unlabeled, limited and sparse datasets, 2) introducing prior linguistic information for regularization (e.g.

thanks to a neural language model) and 3) online adaptation of the conversion model to cope with potential drift in

time of the neural responses.

Requirement and Profile:

PhD in machine learning, signal/image/speech processing

Advanced knowledges in deep learning

Excellent programming skills (mostly Python)

Fluent in English

Duration: 12 months

Salary (before tax) / Month €: Depending on the experience

Starting date: Early 2020

How to apply:

Send a cover letter, a resume, and references by email to:

Dr. Thomas Hueber, thomas.hueber@gipsa-lab.fr

Dr. Laurent Girin, laurent.girin@grenoble-inp.fr

Dr. Blaise Yvert, blaise.yvert@inserm.fr

Applications will be processed as they arise.

Top

6-62(2020-01-10) Pre-Doc RESEARCH CONTRACT (for 3 months, extendable to one year), University odf the Basque Country, Leioa (Bizkaia), Spain
One Pre-Doc RESEARCH CONTRACT (for 3 months, extendable to one year) is open for the study, development, integration and evaluation of machine learning software tools, and the production of language resources for Automatic Speech Recognition (ASR) tasks.

Applications are welcome for one graduate (Pre-Doc) research contract for the study, development, integration and evaluation of machine learning software tools, and the production of language resources for ASR tasks. The contract will be funded by an Excellence Group Grant by the Government of the Basque Country. Initially, the contract is for 3 months but, if performance is satisfactory, it will be extended at least to one year ?or even more, depending on the available budget?, with a gross salary of around 30.000 euros/year. The workplace is located in the Faculty of Science and Technology (ZTF/FCT) of the University of the Basque Country (UPV/EHU) in Leioa (Bizkaia), Spain.

PROFILE

We seek graduate (Pre-Doc) candidates with a genuine interest in computer science and speech technology. It will be required knowledge and skills in any (preferably all) of the following topics: machine learning (specifically deep learning), programming in Python, Java and/or C++ and signal processing. A master's degree in scientific and/or technological disciplines (especially computer science, artificial intelligence, machine learning and/or signal processing) will be highly valued. All candidates are expected to have excellent analysis and abstraction skills. Experience and interest in dataset construction will be also a plus.

RESEARCH ENVIRONMENT

The Faculty of Science and Technology (ZTF/FCT) of the University of the Basque Country (https://www.ehu.eus/es/web/ztf-fct) is a very active and highly productive academic centre, with nearly 400 professors, around 350 pre-doc and post-doc researchers and more than 2500 students distributed in 9 degrees.

The research work will be carried out at the Department of Electricity and Electronics of ZTF/FCT in the Leioa Campus of UPV/EHU. The research group hosting the contract (GTTS, http://gtts.ehu.es) has deep expertise in speech processing applications (ASR, speaker recognition, spoken language recognition, spoken term detection, etc.) and language resource design and collection. If the candidate is interested in pursuing a research career, the contract would be compatible with master studies on the topics mentioned above or even a Ph.D. Thesis project within our research group, and further financing options (grants, other projects) could be explored.

The nearby city of Bilbao has become an international destination, with the Guggenheim Bilbao Museum as its main attractor. Still, though sparkling with visitors from worldwide, Bilbao is a peaceful, very enjoyable medium-size city with plenty of services and leisure options, and mild weather, not so rainy as the evergreen hills surrounding the city might suggest.

APPLICATION

Applications including the candidate's CV and a letter of motivation (at most 1 page) explaining their interest in this position and how their education and skills fit the profile should be sent by e-mail ?using the subject 'GTTS research contract APPLICATION ref. 1/2020'? to Germán Bordel (german.bordel@ehu.eus) by Wednesday, January 29, 2020. The contract will start as soon as the position is filled.
Top



 Organisation  Events   Membership   Help 
 > Board  > Interspeech  > Join - renew  > Sitemap
 > Legal documents  > Workshops  > Membership directory  > Contact
 > Logos      > FAQ
       > Privacy policy

© Copyright 2024 - ISCA International Speech Communication Association - All right reserved.

Powered by ISCA