We are seeking a highly motivated and ambitious post-doctoral researcher for the project of ?socially and culturally aware human-agent interaction,? led by Prof. Yukiko Nakano at Seikei University in Tokyo, Japan. The project is part of a larger government funded project for a human-avatar symbiotic society. The mission of our group (http://iui.ci.seikei.ac.jp/en/) is to research and develop technologies for human-agent/robot interaction, behavior generation, and behavior adaptation by focusing on social and cultural awareness.

Qualifications
- The ideal candidate must have a PhD degree and a strong background in machine learning and human-agent/robot interaction.
- Skills: Good skills in programming, such as Python and C#. Solid knowledge and experience in machine learning and deep learning using PyTorch. Solid knowledge of statistical analysis.
- Preferred qualifications: Programming skill in Unity or any animation engines.
- Research interests: human-agent/robot interaction, behavior generation (gestures, facial expressions, eye gaze, posture etc.), multimodal dialogue systems, social signal processing, multimodal machine learning, avatar communication, cross-cultural communication.

Employment
- Full time position.
- The employment contract can be extended based on the annual evaluation until November 2025 at the longest.
- Start date: after April 2021.
- The salary will be determined based on experience and expertise.
 
Application
Please submit your application by e-mail to y.nakano@st.seikei.ac.jp. Please send your application as soon as possible. The recruitment will end when a person is selected. The application should include;
1. Curriculum vitae including relevant professional experience and knowledge
2. One-page summary of your research background and interests
3. Summary of your Doctoral dissertation

Contact
If you have any questions, please contact Yukiko Nakano (y.nakano@st.seikei.ac.jp).

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There is a fully funded PhD position open at Idiap Research Institute on 'Neural
Architectures for Speech Technology'.

The research will build on work done over the past few years 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. More recently, the focus has been on theoretical underpinnings via rigorous
Bayesian techniques.

Although the project remit is quite open, a significant research thread will be to
factorise current neural vocoders into physiological and probabilistic components; this
will be with a focus on identifying how they may be controlled by external agents such as
dialogue managers. Another possible thread is to examine these models in the context of
speech recognition. In doing this, we hope not only to enable the next generation of
expressive speech recognition and synthesis, but also to make inference about the
underlying physiological mechanisms of speech production and perception.

For more information, and to apply, please follow this link:
 https://www.idiap.ch/education-and-jobs/job-10313

Idiap is located in Martigny in French speaking Switzerland, but functions in English and
hosts many nationalities. PhD students are typically registered at EPFL. All positions
offer quite generous salaries. Martigny is a local art, culture and viticulture hub, 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

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DESCRIPTION

REQUIRED SKILLS

APPLICATION INSTRUCTIONS

ABOUT NAVER LABS

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Speech-NLP Master 2 Internship Year 2020-2021

Speech Segmentation and Automatic Detection of Conflicts in

Political Interviews

LISN – Université Paris-Saclay

Internship for Last Year Engineer or Master 2 Students

Keywords: Machine Learning, Diarization, Digital Humanities, Political Speech, Prosody,

Expressive Speech

Context

This internship is part of the Ontology and Tools for the Annotation of Political Speech

(OOPAIP 2018), a transdisciplinary project funded under the DIM-STCN (Text Sciences and

New Knowledge) by the Regional Council of Ile de France. The project is carried out by the

European Center for Sociology and Political Science (CESSP) of the University of Paris 1

Panthéon-Sorbonne, the National Audiovisual Institute (INA), and the LISN. Its objective is to

design new approaches to develop detailed, qualitative, and quantitative analyzes of political

speech in the French media. Part of the project concerns the study of the dynamics of conflicting

interactions in interviews and political debates, which requires a detailed description and a

large corpus to allow for the models’ generalization. Some of the main challenges concern the

performance of speaker and speech style segmentation, e.g., improving the segmentation accuracy,

detecting superimposed speech, measuring vocal effort and other expressive elements.

Objectives

The main objective of the internship is to improve the automatic segmentation of political

interviews. In this context, we will be particularly interested in the detection of hubbub (strong

and prolonged overlapped speech). More precisely, we would like to extract features from the

speech signal (Eyben et al. 2015) correlated with the level of conflictual content in the exchanges,

based, for example, on the arousal level in the speaker’s voice—intermediate level between

the speech signal analysis and the expressivity description (Rilliard, d’Alessandro, and Evrard

2018)—or vocal effort (Liénard 2019).

The internship will initially be based on two corpora of 30 political interviews manually annotated

in speech turns and speech acts—within the framework of the OOPAIP project. It will begin

with a state of the art review of speech diarization and overlapped speech detection (Chowdhury

et al. 2019). The aim will then be to propose solutions based on recent frameworks (Bredin

et al. 2020) to improve the precise localization of speaking segments, in particular when the

frequency of speaker changes is high.

In the second part of the internship, we will look at a more detailed measurement and prediction

of the conflicting level of exchanges. We will search for the most relevant features to describe the

conflicting level and by adapting or developing a neural network architecture for its modeling.

The programming language used for this internship will be Python. The candidate will have

access to the LISN computing resources (servers and clusters with recent generation GPUs).

Publications

Depending on the degree of maturity of the work carried out, we expect the applicant to:

• Distribute the tools produced under an open-source license

• Write a scientific publication

Conditions

The internship will take place over a period of 4 to 6 months at the LISN (formerly LIMSI) in the

TLP group (spoken language processing). The laboratory is located near the plateau de Saclay,

university campus building 507, rue du Belvédère, 91400 Orsay. The candidate will be supervised

by Marc Evrard (evrard@limsi.fr). Allowance under the official standards (service-public.fr).

Applicant profile

• Student in the last year of a 5-years diploma in the field of computer science (AI is a plus)

• Proficiency in Python language and experience in using ML libraries (Scikit-Learn, Tensor-

Flow, PyTorch)

• Strong interest in digital humanities and political science in particular

• Experience in automatic speech processing is preferred

• Ability to carry out a bibliographic study from scientific articles written in English

To apply: Send an email to evrard@limsi.fr including a résumé and a cover letter.

Bibliography

Bredin, Hervé, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, Marvin

Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, and Marie-Philippe Gill. 2020.

“Pyannote. Audio: Neural Building Blocks for Speaker Diarization.” In ICASSP. IEEE.

Chowdhury, Shammur Absar, Evgeny A Stepanov, Morena Danieli, and Giuseppe Riccardi.

2019. “Automatic Classification of Speech Overlaps: Feature Representation and Algorithms.”

Computer Speech & Language 55: 145–67.

Eyben, Florian, Klaus R Scherer, Björn W Schuller, Johan Sundberg, Elisabeth André, Carlos

Busso, Laurence Y Devillers, et al. 2015. “The Geneva Minimalistic Acoustic Parameter Set

(GeMAPS) for Voice Research and Affective Computing.” IEEE Transactions on Affective

Computing 7 (2): 190–202.

Liénard, Jean-Sylvain. 2019. “Quantifying Vocal Effort from the Shape of the One-Third Octave

Long-Term-Average Spectrum of Speech.” The Journal of the Acoustical Society of America

146 (4): EL369–75.

OOPAIP. 2018. “(Ontologie Et Outil Pour l’annotation Des Interventions Politiques).”

DIM STCN (Sciences du Texte et connaissances nouvelles) Conseil régional d’Ile de

France. http://www.dim-humanites-numeriques.fr/projets/oopaip-ontologie-et-outilspour-

lannotation-des-interventions-politiques/.

Rilliard, Albert, Christophe d’Alessandro, and Marc Evrard. 2018. “Paradigmatic Variation of

Vowels in Expressive Speech: Acoustic Description and Dimensional Analysis.” The Journal

of the Acoustical Society of America 143 (1): 109–22.

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Telecom Paris is hiring an associate professor in machine learning for distributed/multi-view machine listening and audio content analysis


Institut Polytechnique de Paris [1] - Telecom Paris [2], LTCI lab [3], ADASP group [4]


-- Important Dates (UPDATED!)
? *March 20th 2021: closing date*
?    End of April 2021: hearings of preselected candidates


Applications are invited for a permanent (indefinite tenure) faculty position at the Associate Professor level (Maitre de Conferences) in machine learning for distributed/multi-view machine listening and audio content analysis.

-- Context

Telecom Paris [2] is a French public institution for engineering higher education (grande ecole) and scientific research, founded in July 1878. It is a founding member of the Institut Polytechnique de Paris [1], a world-class scientific and technological institution. Located in Palaiseau, at the Plateau de Saclay (Paris outskirts), this Institution is a partnership between Ecole Polytechnique, ENSTA Paris, ENSAE Paris, Telecom Paris and Telecom SudParis, with HEC as a key partner. Students and faculty benefit from close relationships between the different institutions.
The Information Processing and Communication Laboratory (LTCI) [3] is Telecom Paris? in-house research laboratory. Since January 2017, it has continued the work previously carried out by the CNRS joint research unit of the same name. The LTCI was created in 1982 and is known for its extensive coverage of topics in the field of information and communication technologies. The LTCI?s core subject areas are computer science, networks, data science, signal and image processing and digital communications. The laboratory is also active in issues related to systems engineering and applied mathematics.
The open position will be hosted by Telecom Paris? Audio Data Analysis and Signal Processing (ADASP) group [4], a subgroup of the statistics, signal processing and machine learning (S²A) team, within the Images, Data & Signals (IDS) department [5].


-- Main missions

The hired associate professor will be expected to:

[Research activities]
?    Develop research in multi-view/distributed machine learning applied to machine listening, in line with the topics of Telecom Paris? Audio Data Analysis and Signal Processing (ADASP) group
?    Develop both academic and industrial collaborations, including collaborative activities with other Telecom Paris research departments and teams, and research contracts with industrial players
?    Submit proposals to national and international research project calls


[Teaching activities]
?    Participate in teaching activities at Telecom Paris and its partners (as part of joint Master programs), especially in machine learning, signal processing, and machine listening, including life-long training programs (e.g. the local Data Scientist certificate)

[Impact]
?    Publish high quality research work in leading journals and conferences
?    Play an active role in the research communities relevant to the position (serving in scientific committees and boards, organizing seminars, workshops, special sessions...)


-- Candidate profile

As a minimum requirement, the successful candidate will have:

?    A PhD degree
?    A track record of research and publication in one or more of the following areas: machine learning, signal processing or machine listening
?    Experience in deep learning, audio data analysis, machine listening, music data analysis, multi-view learning, distributed learning
?    Experience in teaching
?    Good command of English

The ideal candidate will also (optionally) have:
?    Knowledge in frugal learning techniques
?    Experience in source separation/enhancement and signal denoising techniques
?    Experience in distributed computing environments

Other skills expected include:
?    Capacity to work in a team and develop good relationships with colleagues and peers
?    Good communication and pedagogical skills

-- Place of work

Palaiseau (Paris outskirts), France

-- How to apply (UPDATED!)

The application shall be submitted, through this link: https://institutminestelecom.recruitee.com/o/maitre-de-conference-en-machine-listening, as a single pdf file, including:

?    a letter of motivation
?    a complete and detailed curriculum vitae
?    statements of research and teaching interests (4 pages)
?    three main publications
?    contact information for two references, to be sent to Slim Essid

-- Contact

Slim Essid (Coordinator of the ADASP group), https://perso.telecom-paris.fr/~essid/



[1] https://www.ip-paris.fr/en
[2] https://www.telecom-paris.fr/en/home
[3] https://www.telecom-paris.fr/en/research/laboratories/information-processing-and-communication-laboratory-ltci
[4] https://adasp.telecom-paris.fr
[5] http://www.tsi.telecom-paristech.fr/en/      

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Un poste d'ingénieur contractuel à la section audio de la police technique et scientifique est à pourvoir.
Pour plus d'informations, voici le lien

https://place-emploi-public.gouv.fr/offre-emploi/police-scientifique---ipts--adjoint-au-chef-de-la-section-audio-reference-2021-545548/

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A fully funded PhD position in Deep Learning for Conversational AI

KTH, Royal Institute of Technology, Stockholm, Sweden. Apply here (deadline 2/4)

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:379667/where:4/

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Two  

Two PhD positions are open at NTNU Trondheim, Norway

https://www.jobbnorge.no/en/available-jobs/job/200820/2-phd-positions-in-machine-learning-for-speech-analysis-and-recognition

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 Telecom Paris is hiring an associate professor in machine learning for
distributed/multi-view machine listening and audio content analysis

See offer here:
https://adasp.telecom-paris.fr/news/job_offers/highlights/adasp_position_machine_listening_2021/ or read
on...


Institut Polytechnique de Paris [1] - Telecom Paris [2], LTCI lab [3], ADASP group [4]


-- Important Dates
? *March 20th 2021: closing date*
?    End of April 2021: hearings of preselected candidates


Applications are invited for a permanent (indefinite tenure) faculty position at the
Associate Professor level (Maitre de Conferences) in machine learning for
distributed/multi-view machine listening and audio content analysis.

-- Context

Telecom Paris [2] is a French public institution for engineering higher education (grande
ecole) and scientific research, founded in July 1878. It is a founding member of the
Institut Polytechnique de Paris [1], a world-class scientific and technological
institution. Located in Palaiseau, at the Plateau de Saclay (Paris outskirts), this
Institution is a partnership between Ecole Polytechnique, ENSTA Paris, ENSAE Paris,
Telecom Paris and Telecom SudParis, with HEC as a key partner. Students and faculty
benefit from close relationships between the different institutions.
The Information Processing and Communication Laboratory (LTCI) [3] is Telecom Paris?
in-house research laboratory. Since January 2017, it has continued the work previously
carried out by the CNRS joint research unit of the same name. The LTCI was created in
1982 and is known for its extensive coverage of topics in the field of information and
communication technologies. The LTCI?s core subject areas are computer science, networks,
data science, signal and image processing and digital communications. The laboratory is
also active in issues related to systems engineering and applied mathematics.
The open position will be hosted by Telecom Paris? Audio Data Analysis and Signal
Processing (ADASP) group [4], a subgroup of the statistics, signal processing and machine
learning (S²A) team, within the Images, Data & Signals (IDS) department [5].


-- Main missions

The hired associate professor will be expected to:

[Research activities]
?    Develop research in multi-view/distributed machine learning applied to machine
listening, in line with the topics of Telecom Paris? Audio Data Analysis and Signal
Processing (ADASP) group
?    Develop both academic and industrial collaborations, including collaborative
activities with other Telecom Paris research departments and teams, and research
contracts with industrial players
?    Submit proposals to national and international research project calls


[Teaching activities]
?    Participate in teaching activities at Telecom Paris and its partners (as part of
joint Master programs), especially in machine learning, signal processing, and machine
listening, including life-long training programs (e.g. the local Data Scientist
certificate)

[Impact]
?    Publish high quality research work in leading journals and conferences
?    Play an active role in the research communities relevant to the position (serving in
scientific committees and boards, organizing seminars, workshops, special sessions...)


-- Candidate profile

As a minimum requirement, the successful candidate will have:

?    A PhD degree
?    A track record of research and publication in one or more of the following areas:
machine learning, signal processing or machine listening
?    Experience in deep learning, audio data analysis, machine listening, music data
analysis, multi-view learning, distributed learning
?    Experience in teaching
?    Good command of English

The ideal candidate will also (optionally) have:
?    Knowledge in frugal learning techniques
?    Experience in source separation/enhancement and signal denoising techniques
?    Experience in distributed computing environments

Other skills expected include:
?    Capacity to work in a team and develop good relationships with colleagues and peers
?    Good communication and pedagogical skills

Note that you do *not* need to speak French to apply. 

-- Place of work

Palaiseau (Paris outskirts), France

-- How to apply

The application shall be submitted, through this link:
https://institutminestelecom.recruitee.com/o/maitre-de-conference-en-machine-listening,
as a single pdf file, including:

?    a letter of motivation
?    a complete and detailed curriculum vitae
?    statements of research and teaching interests (4 pages)
?    three main publications
?    contact information for two references, to be sent to Slim Essid

-- Contact

Slim Essid (Coordinator of the ADASP group), https://perso.telecom-paris.fr/~essid/

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[Postdoctoral researcher, Nara Institute of Science and Technology]
Augmented Human Communication Laboratory directed by Professor Satoshi
Nakamura has a postdoctoral research position available in the area of human
information processing, dialogue systems, statistical modeling, machine
learning, and brain science for the Training Adapted Personalised Affective
Social Skills with Cultural Virtual Agents (ANR-CREST: JPMJCR19A).
Affiliation: Division of Information Science, Nara Institute of Science and
Technology, Japan.
Position: Postdoctoral researcher
Recruitment personnel: 1 person
Appointment time: May 1, 2021, or later as early as possible to March 31,
2022.
Term: Contract can be renewed every year. The longest employment is until
March 31, 2025.
Trial period: No trial period

Job description:
The research area is related to the Training Adapted Personalised Affective
Social Skills with Cultural Virtual Agents (ANR-CREST: JPMJCR19A), and
fields such as dialogue system using machine learning, and multimodal information
processing.
Application conditions:
- Doctoral researcher
A person with a Ph.D. who builds a dialogue system using statistical
methods, machine learning, and multimodal information processing. Those who have
the necessary knowledge and experience regarding the area and are willing to
conduct research independently.

Salary: Determined based on the university regulations
Benefits: Join health insurance, pension insurance, accident compensation
insurance, and employment insurance

Workplace:
Augmented Human Communication Laboratory, Nara Institute of Science and
Technology.
Employment period: May 1, 2021, or later as early as possible to March 31,
2022. Contract can be renewed every year. The longest employment is until
March 31, 2025.

Work style:
? Working days: Monday to Friday
? Holidays: Saturdays, Sundays, national holidays, summer holidays,
year-end and new year holidays, and the anniversary of the university
foundation (October 1)
? Working hours: Discretionary work system

Deadline: April 9, 2021 (Friday)


[Application method]
Documents to be submitted:
(1) Resume (using the university's format: see the URL below)
https://www.naist.jp/en/about_naist/job_opportunities/resume_format.html
(2) List of research achievements
(3) Research motivation (A4 one page)
(4) Three major publications
(5) Letters from two references with their address, telephone number and
email address included.
Where to submit application documents:
After stating 'Postdoctoral researcher application' in the title, Please
submit by e-mail to the following contact address.
E-mail: tapas-positions@is.naist.jp

[Selection details (selection method, decision on acceptance / rejection),
result notification]
(1) 1st selection: document screening
(2) 2nd selection: online interview
After screening the documents, we will contact you for an interview.
* Application documents will be used only for the purpose of recruitment
screening and will not be used for any other purpose. The application
documents will not be returned regardless of the result of acceptance or
rejection. In consideration of the risk of personal information leakage due
to misdelivery, application documents for non-employees will be responsibly
deleted at the end of recruitment activities.

Contact:
?630-0192
8916-5 Takayama-Cho, Ikoma, Nara, Japan
Professor Satoshi Nakamura, Augmented Human Communication Laboratory, Nara
Institute of Science and Technology
E-mail:  tapas-positions@is.naist.jp
############################

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Researchers in Speech, Text and Multimodal Machine Translation @ DFKI Saarbrücken, Germany

--------------------------------------------------------------

The MT group at MLT@DFKI Saarbrücken is looking for

     senior researchers/researchers/junior researchers

in speech, text and multimodal machine translation using deep learning.

3 year contracts. Possibility of extension. Ideal starting dates around June/July 2021.

Key responsibilities:
- Research and development in speech, text and multimodal MT
- Scientific publications
- Co-supervision of BSc/MSc students and research assistants
- Possibility of teaching at Saarland University (UdS)
- Senior: PhD co-supervision
- Senior: Project/grant acquisition and management

Qualifications senior researchers/researchers:
- PhD in NLP/Speech/MT/ML/CS or related
- strong scientific and publication track record in speech/text/multimodal-NLP/MT

Qualifications junior researchers:
- MSc in CS/NLP/Speech/ML/MT or related (possibility to do a PhD at
DFKI/UdS)

All:
- Strong background in machine learning and deep learning
- Strong problem solving and programming skills
- Strong communication skills in written and spoken English (German an asset, but not a requirement)

Working environment: the post are in the ?Multilinguality and Language Technology? MLT Lab at DFKI (the German Research Center for Artificial Intelligence https://www.dfki.de/en/web/) in Saarbrücken, Germany. MLT is led by Prof. Josef van Genabith. MLT is a highly international team and does basic and applied research.

Application: a short cover letter indicating which level (senior / researcher / junior) you apply for, a CV, a brief summary of research interests, and contact information for three references. Please submit your application by Friday April 23rd, 2021 as PDF to Prof. Josef van Genabith (josef.van_genabith@dfki.de) indicating your earliest possible start date. Positions remain open until filled.

Selected MT@MLT group publications 2020/21: Xu et al. Probing Word Translation in the Transformer and Trading Decoder for Encoder Layers.
NAACL-HLT 2021. Chowdhury et al. Understanding Translationese in Multi-View Embedding Spaces. COLING 2020. Pal et al. The Transference Architecture for Automatic Post-Editing. COLING 2020. Ruiter et al.
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation. EMNLP-2020. Zhang et al. Translation Quality Estimation by Jointly Learning to Score and Rank. EMNLP 2020. Xu et al. Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change. ACL 2020. Xu et al. Learning Source Phrase Representations for Neural Machine Translation. ACL 2020. Xu et al. Lipschitz Constrained Parameter Initialization for Deep Transformers. ACL 2020. Herbig et al.
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation. ACL 2020. Herbig et al. MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering and Speech Commands for Post-Editing Machine Translation. ACL 2020. Alabi et al. Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi. LREC 2020.
Costa-jussàet al. Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction. In: Computational Linguistics (CL) Special Issue: Multilingual and Interlingual Semantic Representations for Natural Language Processing. Xu et al. Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating. IJCAI 2020

DFKI is one of the leading AI centers worldwide, with several sites in Germany. DFKI Saarbrücken is part of the Saarland University (UdS) Informatics Campus. UdS has exceptionally strong CS and CL schools and, in addition to DFKI, a Max Plank Institute for Informatics, a Max Plank Institute for Software Systems, the Center for Bioinformatics, and the CISPA Helmholz Center for Information Security.

Geographic environment: Saarbrücken (http://www.saarbruecken.de/en) is the capital of Saarland, one of the Federal States in Germany, located right in the heart of Europe and a cultural center in the border region of Germany, France and Luxembourg. Frankfurt and Paris are less than 2 hours by train. Living cost is moderate in comparison with other cities in Germany and Europe.

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**** If you don't read French and are interested in a PhD position in AI/NLP please
contact us directly for further information. French speaking is not required for the
position. ****

 Les réponses doivent nous parvenir de préférence **avant le 10 mai**.

PROPOSITION SUJETS DE THESES

CONTRATS DOCTORAUX 2021-2024

Appel cible (merci de cocher la case correspondante):

X Contrat doctoral ministeriel ED 536

□ Contrat doctoral ministeriel ED 537

------------------------------------------------------------------------------------------------------------------------

Directeur de these : Fabrice LEFEVRE

Co-directeur eventuel :

Co-encadrant eventuel : Bassam JABAIAN

Titre en francais : Transformer et renforcer pour le transfert et l’apprentissage en ligne des

agents conversationnels vocaux

Titre en anglais : Transformer and Reinforce for transfer and online learning of vocal

conversational agents

Mots-cles : IA, natural language processing , human-machine vocal interactions, deep learning,

deep reinforcement learning, transfer learning

Co tutelle : XXX - Non Pays :

Opportunites de mobilite a l’international du doctorant dans le cadre de sa these : oui

Profil du candidat :

Le candidat doit avoir un master en informatique avec une composante sur les méthodes

d'apprentissage automatique et/ou sur l’ingénierie de la langue. La bourse de thèse fera l’objet

d’un concours au sein de l’Ecole Doctorale 536 de l’université d’Avignon, avec une audition du

candidat retenu par les encadrants de thèse.

Pour postuler merci d’envoyer un mail avant le 10 mai 2021 à Fabrice Lefèvre

(fabrice.lefevre@univ-avignon.fr) et Bassam Jabaian (bassam.jabaian@univ-avignon.fr)

incluant : votre CV, une lettre de motivation avec votre positionnement sur les propositions

d’études ci-dessous, d’éventuelles lettres de recommandation et vos relevés de notes.

Presentation detaillee du sujet :

Domaine / Thematique : IA/NLP

Objectif : Permettre le transfert et l'apprentissage en ligne des agents conversationnels vocaux

avec une combinaison Transformers/Renforcement

Contexte et enjeux : Parmi les activités de recherche en intelligence artificielle, améliorer

l'interaction vocale avec les machines reste un défi majeur d’actualité. Le LIA traite de

multiples aspects liés à l’interaction vocale mais cherche à travers cette thèse à approfondir en

particulier la recherche sur les techniques d’apprentissage des agents conversationnels vocaux

à base de réseaux de neurones profonds supervisés et renforcés. De tels agents dialoguant

sont un enjeu primordial afin d’améliorer les capacités de nos sociétés à gérer une

distanciation sociale contrôlée, notamment par la délégation de certaines tâches risquées à

des artefacts matériels efficients, et bien acceptés par le grand public.

Les récentes évolutions en réseaux de neurones ont permis d’élaborer des systèmes de

génération de texte (ou modèles de langage) de grande qualité. Ils sont pour cela appris sur

des quantités gigantesques de documents, mais permettent en contrepartie une couverture

très large du langage humain. Les représentants les plus avancés dans ce domaine sont les

Transformers, qui permettent d’éliminer le recours à la récurrence dans les réseaux (couteux

en calcul) en privilégiant un mécanisme d’attention démultipliée (multi-head self-attention).

De nombreux dérivés de ces modèles existent et ont permis des gains conséquents en

performance sur de nombreuses tâches impliquant la génération de texte en langage naturel.

Ainsi BERT [1] et GPT forment les grandes familles (et leurs multiples descendants distilBERT,

alBERT, GPT-2…). Mais si de tels modèles permettent de porter à un plus haut niveau de

performance nos capacités de modélisation du langage, il reste encore à savoir les mettre en

oeuvre pour des tâches plus spécifiques ou exigeantes, comme les systèmes d’interaction

orale.

Ainsi le problème de leur application au cas des agents conversationnels reste ouvert car à la

fois l’interaction directe avec les humains accentue l’impact des erreurs et imperfections des

modèles et d’autre part la gestion des interactions se fait dans un contexte finalisé, où

l’objectif n’est pas le simple échange de données langagières mais la réussite d’un objectif

latent (obtenir une information précise, réaliser ou faire réaliser une action…). Aussi le

challenge principal que nous souhaitons porter dans la thèse est de permettre une adaptation

sur une tache particuliere des capacites d’un Transformer pre-entraine, notamment pour

l’elaboration d’un agent conversationnel. Des approches par transfert d’apprentissage ont

déjà été initiées mais leurs résultats sont contrastés et doivent être renforcés [2]. Nous

identifions deux axes majeurs pour la thèse :

Axe 1/ Transfert et apprentissage en ligne / Tout d’abord les approches de transfert reposent

toujours sur le recours à de nouvelles données pré-collectées auxquelles sont confrontés les

modèles [2]. Ainsi, dans la continuité de nos précédents travaux sur l’apprentissage en ligne

des systèmes de dialogue, nous souhaiterions élaborer et évaluer des strategies efficaces pour

permettre le recours a des apprentissages par renforcement [3, 4]. Pour rendre les systèmes

artificiels capables d'apprendre à partir des données, deux hypothèses fortes sont

généralement faites : (1) la stationnarité du système (l'environnement de la machine ne

change pas avec le temps), (2) l'interdépendance entre la collecte des données et le processus

d'apprentissage (l'utilisateur ne modifie pas son comportement dans le temps). Or les

utilisateurs ont une tendance naturelle à adapter leur comportement en fonction des réactions

de la machine, ce qui gêne la convergence de l'apprentissage vers un équilibre lui permettant

de satisfaire en permanence les attentes de l'utilisateur. Aussi les interfaces vocales doivent

évoluer vers une nouvelle génération de systèmes interactifs, capables d'apprendre

dynamiquement sur le long terme à partir d'interactions, tout en anticipant les variations du

comportement des humains, étant eux-mêmes vu comme des systèmes évolutifs.

L’enjeu est alors, dans le contexte de l’apprentissage par renforcement profond [5] de pouvoir

démontrer l’optimalité de la convergence des algorithmes utilisés pour mettre à jour les poids

de certaines couches du modèle au fur et à mesure des interactions avec des utilisateurs, sans

prendre le risque d’une atténuation des performances initiales. La détermination optimale des

paramètres à modifier doit pouvoir être automatisée. Ce projet s’inscrit aussi dans le cadre de

l’apprentissage en continu (continual learning) [6] d’un agent conversationnel.

Axe 2/ Modelisation de l’oral / Ensuite l’essentiel des modèles pré-cités modélisent

exclusivement le langage écrit et intègrent peu de mécanismes dédiés à la nature du langage

parlé. Aussi nous souhaiterions augmenter les capacités de telles machines à faire face à : 1)

des entrées utilisateurs plus naturelles, et comprenant donc de nombreux écarts vis-à-vis de

l’écrit (agrammaticalité, confusions, reprises, corrections, hésitations…) et 2) des erreurs dans

les transcriptions dues au composant de reconnaissance de la parole. Il est donc nécessaire de

pouvoir interfacer le composant d’analyse de la parole avec la chaine de modelisation du

langage qui suit (analyse sémantique, suivi de l’état de dialogue, gestion du dialogue,

génération et synthèse de parole) de manière à prendre en compte les multiples hypotheses

realistes (et non plus seulement la meilleure). Et enfin permettre un arbitrage entre ces

hypothèses qui prenne en compte les traitements suivants, en conformité avec le processus

cognitif humain équivalent (capable de re-traiter ses hypothèses acoustiques les plus

probables en cas de conflit avec ses inférences sémantiques).

Cette étude pourra être menée dans plusieurs cadres applicatifs, à préciser au démarrage de la

thèse : par exemple un robot Pepper dialoguant affecté à la gestion de l’accueil d’un lieu public

(par exemple dans un hôpital ou un musée). Il sera alors possible de déléguer des tâches de

premier contact et d’orientation à des artefacts insensibles aux transmissions biologiques, ce

qui constitue un atout hautement stratégique afin d’améliorer la gestion d’une situation de

crise, du type de la pandémie mondiale de coronavirus en cours.

[1] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional

Transformers for Language Understanding,” arXiv.org, Oct. 2018.

[2] T. Wolf, V. Sanh, J. Chaumond, and C. Delangue, “TransferTransfo: A Transfer Learning

Approach for Neural Network Based Conversational Agents,” arXiv.org, Jan. 2019.

[3] E. Ferreira, B. Jabaian, and F. Lefèvre, “Online adaptative zero-shot learning spoken

language understanding using word-embedding,” in Proceedings of 2015 IEEE International

Conference on Acoustics, Speech and Signal Processing, ICASSP 2015, 2015, pp. 5321–5325.

[4] M. Riou, B. Jabaian, S. Huet, and F. Lefèvre, “Joint On-line Learning of a Zero-shot Spoken

Semantic Parser and a Reinforcement Learning Dialogue Manager,” in IEEE International

Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, Brighton, United

Kingdom, May 12-17, 2019, 2019, pp. 3072–3076.

[5] K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “A Brief Survey of Deep

Reinforcement Learning,” IEEE SIGNAL Process. Mag. Spec. ISSUE Deep Learn. IMAGE Underst.,

Aug. 2017.

[6] Z. Chen and B. Liu, Lifelong Machine Learning, Second Edition, vol. 12, no. 3. Morgan &

Claypool Publishers, 2018.

Les sujets devront être adressés à

secretariat-ed@univ-avignon.fr

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POSITION: Director, Center for Language and Speech Processing

REPORTS TO: Ed Schlesinger, Benjamin T. Rome Dean Johns Hopkins University, Whiting School of Engineering

INSTITUTION: Johns Hopkins University, Baltimore, MD https://engineering.jhu.edu/

                                                               2.23.21

The Whiting School of Engineering at Johns Hopkins University invites nominations and applications for the position of Director of the Center for Language and Speech Processing (CLSP). The Director will be appointed as a full-time tenured faculty member in the Whiting School of Engineering and will be encouraged to remain active in research, with strategic leadership of the Center as their top priority. This is an outstanding opportunity for an accomplished scholar with leadership experience to further strengthen an exceptional interdisciplinary research center at the nation’s first research university. The best candidates will embody the intellectual distinction, entrepreneurial capacity, collaborative spirit, transparency, inclusiveness, and creativity that characterize the School’s culture and will bring a scholarly record deserving appointment as tenured professor at The Johns Hopkins University.

The Center for Language and Speech Processing

CLSP is one of the Whiting School’s 25 Interdisciplinary Centers and Institutes. The Center currently comprises over 25 tenure-line and research faculty whose primary appointments are in the Whiting School of Engineering or in other closely related schools, along with over 70 PhD students. CLSP was established in 1992 and grew to prominence under the directorship of the late Frederick Jelinek. It aims to understand how human language is used to communicate ideas, and to develop technology for machine analysis, translation, and transformation of multilingual speech and text. In 2007 CLSP gained a sibling, the national Human Language Technology Center of Excellence (https://hltcoe.jhu.edu), a governmentfunded research center at Johns Hopkins that develops critical speech and language technology for government use; several HLTCOE researchers are tightly integrated into CLSP. Recently, CLSP has further expanded its research portfolio by adding several prominent researchers in computer vision and related fields. As part of its educational mission, CLSP coordinates a full complement of courses dealing with a diverse array of topics in language and speech. It offers a weekly seminar featuring prominent visiting speakers in speech and language processing. It also runs the Fred Jelinek Memorial Workshop in Speech and Language Technology (JSALT), a widely-known residential research workshop that annually assembles teams of researchers from around the world to spend 6 summer weeks conducting intensive research on fundamental problems. Held annually since 1995, the workshop has produced many important advances in speech and language technology.

Opportunities for the Center Director

The CLSP Director will work with colleagues in and beyond CLSP to increase its impact by both enhancing its historic strengths and positioning it as a central element of a set of AI-related initiatives across the Whiting School and the University more broadly. To these ends, the Director will identify ways in which the Center will continue to grow and evolve and through which the Center, the Whiting School, and Hopkins can recruit, sustain, and deploy the human and financial resources needed to further distinguish itself.The Director will work to maintain the Center’s position as the disciplinary and intellectual hub of language and speech processing research within the University, enabling CLSP to contribute to and benefit from the success of significant institutional investment in artificial intelligence and machine learning more broadly, including potential applications to key societal problems such as healthcare and scientific endeavors such as linguistics and neuroscience. Collaborations with the Applied Physics Lab (www.jhuapl.edu) present opportunities to bring additional resource, expertise, and scale to advance CLSP research including potentially in classified research. Beyond Hopkins, CLSP’s Director will foster connections with industry as part of the Center’s efforts to expand its base of resources and relationships, to disseminate knowledge and discoveries, and to develop and transfer technologies that may have an impact in the world. In these various external activities, the Director will work with the University’s technology ventures office (https://ventures.jhu.edu), with faculty and students, and with alumni and donors. Specific strategies for enhancing CLSP’s strengths, broadening its impact, and positioning it relative to Hopkins-wide initiatives, along with measures of success and the prioritization of activities designed to achieve success, will be developed by the Director in collaboration with CLSP’s faculty and the Dean.

Diversity, equity, and inclusion at the Whiting School

WSE has a stated commitment to diversity, equity, and inclusion: “Diversity and inclusion enrich our entire community and are critical to both educational excellence and to the advancement of knowledge. Discovery, creativity, and innovation flourish in an environment where the broadest range of experiences are shared, where all voices are heard and are valued, and where individuals from different cultures and backgrounds can collaborate freely to understand and solve problems in entirely new ways.” As the leader of the Center and within the School, CLSP’s Director will work to enhance and expand diversity and inclusion at all levels and will ensure that the Center is a welcoming and supportive environment for all.

Position Qualifications

The new Director will be a proven, entrepreneurial leader who can bring faculty, staff, and students together to pursue a compelling vision of CLSP as an international hub for Language and Speech Processing research and as a site of innovation, teaching, and translation. They will have strong skills for mentoring junior faculty and will promote the interests of the Center. Intellectual curiosity and fundraising experience are valued. They will have a dossier that represent a distinguished track record of scholarship and teaching; a passionate commitment to research, discovery, and application; and an interest in and success at academic administration. Expected educational background and qualifications include:

• An earned doctorate in an area such as electrical and computer engineering, computer science, or a closely related field and a scholarly record deserving appointment as tenured professor at The Johns Hopkins University;

• Recognized leadership in their respective field with a distinguished national and international reputation for research and education;

• Excellent communication skills in both internal and external interactions;

• Strong commitment to diversity and inclusion at all levels among faculty, students, and staff, along with measurable and sustained impact on the diversity and inclusiveness of organizations they have led or been part of; and

• Leadership and administrative experience within a complex research environment or in national/international organizations connected to their respective field.

                                                                     *

The Whiting School of Engineering has engaged Opus Partners (www.opuspartners.net) to support the recruitment of the CLSP Director. Craig Smith, Partner, and Jeff Stafford, Senior Associate, are leading the search. Applicants should submit their CV and a letter of interest outlining their research and leadership experience to Jeffrey.stafford@opuspartners.net. Nominations, expressions of interest, and inquiries should go to the same address. Review of credentials will begin promptly and will continue until the appointment is finalized. Every effort will be made to ensure candidate confidentiality. The Whiting School of Engineering and CLSP are committed to building a diverse educational environment, and women and minorities are strongly encouraged to apply. Johns Hopkins University is an equal opportunity employer and does not discriminate on the basis of gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, other legally protected characteristics or any other occupationally irrelevant criteria. The University promotes Affirmative Action for minorities, women, individuals who are disabled, and veterans. Johns Hopkins University is a drug-free, smoke-free workplace.

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Offre de thèse CIFRE: Système dialogique de questions-réponses contrôlé : application aux forums sur la santé des femmes

Laboratoire d'Informatique de Grenoble / Université Grenoble Alpes (http://lig-getalp.imag.fr/), Grenoble

Société Shesmet (https://www.shesmet.com), Paris

L?objectif de cette thèse de doctorat est de concevoir des méthodes permettant à un système de dialogue de répondre précisément à une question concernant la santé intime des femmes. En effet, la santé génésique et sexuelle des femmes est un sujet encore trop peu abordé dans son ensemble et trop souvent résumé à la santé reproductive. Pourtant les femmes ont physiologiquement plusieurs étapes de vie qui vont impacter de manière plus ou moins forte leur bien-être mental et physique : la puberté, la maternité, la ménopause et l?après ménopause. La santé sexuelle des femmes est aussi un enjeu de politique publique qui a évolué au cours des ans et qui reste au c?ur des problématiques de notre société : précarité menstruelle, contraception, accès à l?IVG, violences sexuelles. L?accès à une information de qualité, personnalisée et en tout anonymat est un fort vecteur d?autonomisation et d?égalité de soins pour l?ensemble de la population féminine. Pourtant, aujourd'hui les femmes voulant se renseigner sur ces thèmes sont souvent en prise avec un flot d'informations qui peuvent être discordantes, incomplètes et de sources non vérifiables (p.ex., les forum de santé alimentés par les utilisateurs). C'est pourquoi Shesmet et le laboratoire d'informatique de Grenoble (LIG) s'associent pour proposer une méthode dialogique de question réponse qui permette d'adapter une réponse experte et vérifiée au contexte particulier d'une question de santé exprimée par une utilisatrice. Cette approche est originale dans le sens ou elle tire partie du meilleur des capacités humaines (réponses pertinente et sans erreur) et computationnelles (capacité des modèles profonds à traiter des données à grande échelle).

Objectif de la thèse

Au cours de la dernière décennie, les systèmes traitement automatique du langage naturel ont fait de grands progrès grâce à l'émergence de l'apprentissage profond. La technique est aujourd'hui suffisamment mature pour être intégrée dans les assistants personnels  [Chen et Gao, 2017] et les systèmes de Question/Réponse. L'architecture actuelle des réseaux neuronaux comprend les RNN (LSTM/GRU) [Hochreiter et Schmidhuber, 1997 ; Cho et al., 2014] et les transformer [Vaswani et al., 2017], en combinaison avec les mécanismes d'attention [Bahdanau et al., 2014] pour permettre l'utilisation d'informations contextuelles allant au-delà d'un seul ou de quelques tours de dialogue [Bothe et al., 2018]. Cependant, ces corpus sont entraînés sur des masses de données tellement grandes et peu contrôlées que les modèles ont tendance à reproduire les comportements de ces données. Par exemple, les grands corpus de journaux généralistes font généralement la part belle au genre masculin. De même les systèmes de question/réponse sont généralement limités à trouver un extrait dans un grand corpus ou à générer une réponse à partir d'un modèle profond. Contrairement à ces systèmes de question réponses classiques, l'objectif sera ici de utiliser l'expertise de spécialistes en santé pour adapter une réponse au contexte de la question [Wu2019]. Ainsi, les experts humains conçoivent des réponses de grande qualité et vérifiées tandis que les systèmes profonds les adaptent aux plus grands nombre en évitant les erreurs usuelles des modèles profonds.

La tâche est donc de concevoir un système capable :

1. de classifier les énoncés du dialogue et les associés à un ensemble de réponses pré-établies ;

2. d?éditer les réponses pré-établie afin de les adapter à la question et au contexte dialogique ;

3. d'estimer le degré de réassurance nécessaire à insérer dans la réponse ;

4. d'expliquer les réponses données.

Dans le cadre de ce programme indicatif de travail, ce doctorat intéressera aux verrous suivants.

• Des domaines peu doté : Il existe peu de corpus accessible hormis les données disponibles au sein de l'entreprise. Une piste de recherche sera d'utiliser les modèles pré-entrainés du LIG sur le français (dont Flaubert, [Le2020], modèle Bert pour le français que l'équipe GETALP a largement contribué a développer) qui est disponible via la bibliothèque Transformer de Hugging Face qui sera transférée [Wolf et al 2019] à la nouvelle tâche de conversation.

• Des biais de modèles. En effet, le sujet se prête à l?analyse d?un enjeu sociétal propre au développement des TAL : la prise en compte des biais de genre face à une population cible principalement féminines. Le LIG a développé une expertise sur ce problème tant du point de vue des modèles textuels qu'oraux [Garnerin2020].

• La contextualisation en dialogue. Dans un forum, l'interaction ne peut être assumée comme étant dyadique (plus de deux personnes) comme dans le dialogue classique (1 personne + un système) dialogue. Comment prendre en compte la contribution de plusieurs intervention pour personnaliser la réponse à faire à une seule personne reste un problème ouvert.

• Explicabilité. Afin de garantir la transparence du système et de permettre aux utilisatrices d?interpréter les réponses fournies, le système doit être en mesure d'expliquer pourquoi une réponse précise à été donnée. Une technique est de fournir les éléments du dialogue qui sur lesquels la réponse a été sélectionnée et adaptée [Atanasova2020] mais d'autres méthodes pourront être explorées.

Environnement scientifique

La thèse sera menée au sein de l'équipe Getalp du laboratoire LIG (https://lig-getalp.imag.fr/). La personne recrutée sera accueillie au sein de l?équipe qui offre un cadre de travail stimulant, multinational et agréable. Par ailleurs, la personne recrutée passera un temps significatif au sein de l'entreprise Shesmet. Shesmet est une startup en e-santé travaillant à la fois sur des projets de recherche et développement et sur des missions d?accompagnement autour de l?innovation en santé auprès d?institutionnels en santé, publics et privés. La société a lancé en 2020 My S Life, une plateforme d'information en santé intime et sexuelle de la femme (www. myslife.co)

Les moyens pour mener à bien le doctorat seront assurés tant en ce qui concerne les missions en France et à l?étranger qu?en ce qui concerne le matériel (ordinateur personnel, accès aux serveurs GPU du LIG, Grille de calcul Jean Zay du CNRS).

Comment postuler ?

Les candidats doivent être titulaires d'un Master en informatique ou en traitement automatique du langage naturel (ou être sur le point d'en obtenir un). Ils doivent avoir une bonne connaissance des méthodes d?apprentissage automatique et idéalement une expérience en collecte et gestion de corpus. Ils doivent également avoir une bonne connaissance de la langue française. Une expérience dans le domaine du dialogue, des systèmes question réponse ou la génération automatique de textes serait un plus.

Les candidatures sont attendues jusqu'au 3 mai 2021. Elles doivent contenir : CV + lettre/message de motivation + notes master + lettre(s) de recommandations; et être adressées à François Portet (Francois.Portet@imag.fr), Didier Schwab (Didier.Schwab@imag.fr) et Juliette Mauro (juliette.mauro@shesmet.com).

References

[Atanasova2020] P Atanasova, JG Simonsen, C Lioma, I Augenstein A Diagnostic Study of Explainability Techniques for Text Classification. Proceedings of EMNLP 2020

[Bahdanau2014] D Bahdanau, K Cho, Y Bengio. 'Neural machine translation by jointly learning to align and translate', arXiv preprint arXiv:1409.0473, 2014

[Bothe2018] Chandrakant Bothe, Cornelius Weber, Sven Magg, Stefan Wermter 'A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks', LREC 2018.

[Chen2017] Yun-Nung Chen, Jianfeng Gao, Open-Domain Neural Dialogue Systems, IJCNLP 2017

[Cho2014] Cho K., van Merrienboer B., Gülçehre Ç., Bougares F., Schwenk H., Bengio Y., « LearningPhrase Representations using RNN Encoder-Decoder for Statistical Machine Translation », CoRR, 2014.

[Garnerin2020] Mahault Garnerin, Solange Rossato, Laurent Besacier: Gender Representation in Open Source Speech Resources. LREC 2020: 6599-6605

[Hochreiter1997] Hochreiter S., Schmidhuber J., « Long Short-Term Memory »,Neural Comput., vol. 9, no8,p. 1735-1780, November, 1997

[Le2020] Le, Hang and Vial, Loic and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabbé, Benoit and Besacier, Laurent and Schwab, Didier (2020) FlauBERT: Unsupervised Language Model Pre-training for French, Proceedings of The 12th Language Resources and Evaluation Conference, Marseille, France, 2479--2490. https://github.com/getalp/Flaubert

[ParlAI] https://parl.ai/docs/tutorial_basic.html

[Vaswani2017] A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez, et al. 'Attention is all you need', 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

[Wolf2019] Thomas Wolf and Victor Sanh and Julien Chaumond and Clement Delangue (2019) TransferTransfo: {A} Transfer Learning Approach for Neural Network Based Conversational Agents, arxiv, 2019 https://github.com/huggingface/transfer-learning-conv-ai

[Wu2019] Wu, Y., Wei, F., Huang, S., Wang, Y., Li, Z., & Zhou, M. (2019, July). Response generation by context-aware prototype editing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 7281-7288).

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Ingénieur.e développement - Inria Bordeaux Sud-Ouest

Thématique : Conception d’une architecture logicielle pour une application en

apprentissage statistique (analyse et classification des voix pathologiques)

Type de contrat : CDD

Début : à partir du 1er juin 2021 et jusqu’au 31 juillet 2021 (possibilité de prolongation)

Date limite de candidature : 15 mai 2021

Lieu : Inria Bordeaux Sud-Ouest

Niveau de diplôme exigé : Bac + 5 ou équivalent

Autre diplôme apprécié : thèse de doctorat

Fonction : Ingénieur scientifique contractuel

Niveau d'expérience souhaité : 3 à 12 ans

Salaire brut mensuel : 2632€ à 3543€, selon diplômes et expérience professionnelle acquise sur poste similaire

Responsable : Khalid Daoudi

Contexte et atouts du poste

Inria, institut national de recherche dédié au numérique, promeut l’excellence scientifique au service du

transfert technologique et de la société.

Inria emploie 2700 collaborateurs issus des meilleures universités mondiales, qui relèvent les défis des sciences

informatiques et mathématiques. Son modèle agile lui permet d’explorer des voies originales avec ses partenaires

industriels et académiques, et de répondre aux enjeux pluridisciplinaires et applicatifs de la transition numérique.

Engagé auprès des acteurs de l’innovation, Inria crée les conditions de rencontres profitables entre recherche

publique, R&D privée et entreprises. Inria transfère vers les startup, les PME et les grands groupes ses résultats et ses

compétences, dans des domaines tels que la santé, les transports, l’énergie, la communication, la sécurité et la protection

de la vie privée, la ville intelligente, l’usine du futur... Inria développe aussi une culture entrepreneuriale ayant conduit à

la création de 120 startup.

Le centre Inria Bordeaux Sud-Ouest est un des neuf centres d’Inria et compte 20 équipes de recherche. Le

centre Inria est un acteur majeur et reconnu dans le domaine des sciences numériques. Il est au coeur d’un riche

écosystème de R&D et d’innovation : PME fortement innovantes, grands groupes industriels, pôles de compétitivité,

acteurs de la recherche et de l’enseignement supérieur.

GEOSTAT est une équipe de recherche Inria dont la thématique de recherche est le traitement de signaux

naturels complexes, notamment en biophysique (geostat.bordeaux.inria.fr/).

Mission confiée

Plusieurs maladies et pathologies peuvent causer des dysfonctionnements ou des altérations dans la production

de la parole. Les plus connues sont les maladies neurodégénératives (telles que les maladies de Parkinson et

d’Alzheimer) et les maladies respiratoires (telles que l’asthme, la BPCO ou la Covid-19). On parle alors de troubles de

la parole ou de parole pathologique.

Il est maintenant établi que certaines de ces maladies se caractérisent par une manifestation précoce des

troubles de la parole. Le développement de biomarqueurs objectifs vocaux est devenu ainsi un enjeu majeur pour l’aide

au diagnostic et suivi de ces maladies. La mission de l’ingénieur(e) recruté(e) s’inscrit dans ce cadre.

L’objectif de la mission est de concevoir une architecture logicielle, en Python, pour :

1 développer une boîte à outils générique de traitement du signal dédiée à l’analyse de la parole pathologique ;

2 implémenter un biomarqueur vocal de la fonction respiratoire en utilisant des techniques d’apprentissage

statistique, dont le Deep Learning.

Cette dernière tâche s’inscrit dans le cadre d’un projet de recherche clinique en partenariat avec l’AP-HP

(Assistance Publique - Hôpitaux de Paris), notamment le service de pneumologie et de réanimation de L'hôpital La

Pitié-Salpêtrière. Le but de ce projet est le développement d’un biomarqueur vocal de l’état respiratoire et de son

évolution pour l’aide au télé-suivi de patients atteints d’une affection respiratoire, dont la Covid-19.

Principales activités

Pour des raisons de sécurité et de confidentialité, les données vocales et cliniques des patients sont hébergées

sur les serveurs EDS (Entrepôt de Données de Santé) de l’AP-HP.

La première tâche sera ainsi de développer une API permettant la communication avec l’infrastructure

d’hébergement.

La deuxième tâche sera d’implémenter des techniques éprouvées d’analyse de la parole pathologies puis

d’autres issus de recherches récentes. Cette tâche s’appuiera, le cas échéant, sur Parselmouth

(parselmouth.readthedocs.io/en/stable/) qui est une librairie Python pour Praat (www.fon.hum.uva.nl/praat/).

La troisième étape consistera à implémenter et expérimenter des techniques d’apprentissage statistique en

utilisant les données de patients. Cette tâche s’appuiera sur les framework habituels de Machine Learning (TensorFlow,

PyTorch, Scikitlearn).

Encadrement

L’ingénieur.e disposera d’un encadrement scientifique, par Khalid Daoudi de l’équipe GEOSTAT, et technique

par Dan Dutartre et François Rué du Service d'Expérimentation et de Développement (SED) d’Inria-Bordeaux.

Compétences

Être titulaire d’un diplôme d’ingénieur et/ou doctorat en sciences du numérique

Disposer d’une expérience significative dans le développement ou le pilotage d’un projet logiciel en python.

. Disposer d’une formation solide en apprentissage statistique (Machine Learning) ainsi que d’une expérience notable

dans ce domaine ;

. Disposer d’une expertise solide en développement logiciel pour être en capacité de s’adapter à différents types

langages des plus standards (Python, C, C++) ; une forte compétence en python est requise ;

. Des connaissances en traitement du signal seraient un plus très apprécié ;

. Maîtriser les concepts, la méthodologie et les outils de la qualité logicielle ;

. Maîtriser les méthodologies de gestion de projet logiciel collaboratif ;

. Maîtriser les méthodologies d’architectures logicielles modulaires ;

. Excellent relationnel ;

. Savoir travailler en équipe pluridisciplinaires ;

. Savoir s’adapter au contexte projet ;

. Être autonome dans son organisation personnelle et le reporting ;

. Avoir une bonne communication écrite et orale en français ;

. Maîtriser l’anglais technique et scientifique.

Candidature

Le(a) candidat(e) est invité(e) à envoyer sa candidature à khalid.daoudi@inria.fr ; francois.rue@inria.fr ;

dan.dutartre@inria.fr

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Proposal for a postdoctoral position at INRIA, Bordeaux, France

Title: Sparse predictive models for the analysis and classification of pathological speech

Keywords: Pathological speech processing, Sparse modeling, Optimization algorithms, Machine learning,

Parkinsonian disorders, Respiratory diseases

Contact and Supervisor: Khalid Daoudi (khalid.daoudi@inria.fr)

INRIA team: GEOSTAT (geostat.bordeaux.inria.fr)

Duration: from 01/11/2021 to 31/12/2022 (could be extended to an advanced or a permanent position)

Salary: 2653€ / month

Profile: PhD degree obtained after August 2019 or to be defended by the end of 2021. High quality applications

with a PhD obtained before August 2019 could be considered for an advanced research position.

Required Knowledge and background: A solid knowledge in speech/signal processing; A good mathematical

background; Basics of machine learning; Programming in Matlab and Python.

Scientific research context

During this century, there has been an ever increasing interest in the development of objective vocal biomarkers

to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of

the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a

class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s

disease. However, relatively few studies have addressed the challenging problem of discrimination between

subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages

[2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing

developments in very specialized areas) but is taking a great attention since the beginning of the pandemic.

On the other hand, the large majority of existing processing methods (of pathological speech in general) still

heavily rely on a core of feature estimators designed and optimized for healthy speech. There exist thus a strong

need for a framework to infer/design speech features and cues which remain robust to the perturbations caused

by (classes of) disordered speech. The first and main objective of this proposal is to explore the framework of

sparse modeling of speech which allow a certain flexibility in the design and parameter estimation of the sourcefilter

model of speech production. This exploration will be essentially based on theoretical advances developed

by the GEOSTAT team and which have led to a significant impact in the field of image processing, not only at

the scientific level [3] but also at the technological level (www.inria.fr/fr/i2s-geostat-un-innovation-lab-enimagerie-

numerique).

The second objective of this proposal is to use the resulting representations as inputs to basic machine learning

algorithms in order to conceive a vocal biomarker to assist in the discrimination between subgroups of

Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in

the monitoring of respiratory diseases (Covid-19, Asthma, COPD).

Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of

two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian

disorders) and in Paris (for respiratory diseases).

Work description

As stated above, the work to be carried is decomposed in two parts. The main part consists in developing new

algorithms, based on sparse modeling, for the analysis of a class of disordered speech. The second part consists

in exploring machine learning tools to develop vocal biomarkers for the purpose of (differential) diagnosis and

monitoring of the diseases under study.

1. Sparse modeling for disordered speech analysis

The first task will be to investigate sparsity in the framework of linear prediction modeling of speech. The latter

is indeed one of the building blocks for the estimation of core glottal, phonation and articulatory features. Sparse

linear prediction (SLP) has been recently investigated in a convex setting using the L1-norm and applied,

essentially, to speech coding [4]. We will start by investigating the potential of this convex setting in disordered

speech analysis. We will then explore the use of non-convex penalties that allow sparsity control and a better

decoupling the vocal tract filter from excitation source. We will study the spectral properties of the different

models and revisit a set of acoustic features which are not robust to perturbations raising in dysarthric speech.

We will then explore the potential of SLP in designing new features which could be informative about dysarthria.

The algorithmic developments will be evaluated using a rich set of biosignals obtained from patients with

Parkinsonian disorders and from healthy controls. The biosignals are electroglottography and aerodynamic

measurements of oral and nasal airflow as well as intra-oral and sub-glottic pressure.

After dysarthria analysis, we will study speech impairments caused by respiratory deficits. The main goal here

will be to automatically identify respiratory patterns and to design features to quantify the impairments. The

developments will be evaluated using manual annotations, by an expert phonetician, of speech signals obtained

from patients with respiratory deficit and from healthy controls.

Depending on the work progress and time constraints, we may also explore sparsity beyond the linear prediction

model through existing nonlinear representations of speech. It is well known indeed that the linear source-filter

model of speech cannot capture several nonlinearities which exist in the speech production process, particularly

in disordered speech.

2. Machine learning for disease diagnosis and monitoring

Using the outcomes of the first part, the (experimental) objective of the second part is to apply basic machine

learning algorithms (LDA, logistic regression, decision trees, SVM…) using standard tools (such as Scikit-

Learn) to conceive robust algorithms that could help, first, in the discrimination between Parkinsonian disorders

and, second, in the monitoring of respiratory deficit.

3. Work synergy

- The postdoc will interact closely with an engineer who is developing an open-source software architecture

dedicated to pathological speech processing. The validated algorithms will be implemented in this architecture

by the engineer, under the co-supervision of the postdoc.

- Giving the multidisciplinary nature of the proposal, the postdoc will interact with the clinicians participating in

the two clinical studies.

References:

[1] J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013.

[2] J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive

supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015.

[3] H. Badri. Sparse and Scale-Invariant Methods in Image Processing. PhD thesis, University of Bordeaux,

France, 2015.

[4] D. Giacobello et al. Sparse Linear Prediction and Its Applications to Speech Processing. IEEE Transactions

on Audio Speech and Language Processing, (20)5, 2012.

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The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context, acting as the distribution agency of the European Language Resources Association (ELRA), is currently seeking to fill an immediate vacancy for a Technical Engineer position.

Job description
Under the supervision of the CEO, the responsibilities of the Technical Engineer include planning and implementing technical development of tools, software components or applications for language resource production and management.
He/she will be in charge of contributing in the current language resources production workflows and managing 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.

The position is based in Paris 13.

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

Required profile
?    Master 2 or 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
?    Good level of English, with strong writing and documentation skills
?    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
?    Proficiency in Python
?    Knowledge and hands-on in XML and Json
?    Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)

About
ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA/ELRA, visit: http://www.elda.org

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to:

ELDA
9 rue des Cordelières
75013 Paris FRANCE
Email: job@elda.org

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The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Web Developer position.

Job description
Under the supervision of the technical department manager, the responsibilities of the Web Developer consist in designing and developing web applications and software tools for linguistic data management.
Some of these software developments are carried out within the framework of European research and development projects and are published as free software.
Depending on the profile, the Web Developer could also participate in the maintenance and upgrading of the current linguistic data processing toolchains, while being hands-on whenever required by the language resource production and management team.

The position is based in Paris 13.

Salary: Commensurate with qualifications and experience (between 36-45K?).
Other benefits: complementary health insurance and meal vouchers

Required profile
?    Master (BAC + 5 or higher) in Computer Science or a related field (experience in natural language processing is a strong plus)
?    Proficiency in Python
?    Hands-on experience in Django
?    Hands-on knowledge of a distributed version control system (Git preferred)
?    Knowledge of SQL and of RDBMS (PostgreSQL preferred)
?    Basic knowledge of JavaScript and CSS
?    Basic knowledge of Linux shell scripting
?    Practice of free software
?    Proficiency in French and English
?    Curious, 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

About
ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA/ELRA, visit: http://www.elda.org

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to:
ELDA
9 rue des Cordelières
75013 Paris FRANCE
Email: job@elda.org

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Recherche de « jeunes docteurs en 1er CDI»

en linguistique, didactique des langues

ET en Natural Language Processing

La société LUDO-VIC a pour devise :

« Quels que soient votre langue maternelle et votre niveau de scolarisation, apprenez les bases de n’importe quels

concepts : une nouvelle langue, des gestes de santé/sécurité, du savoir-être, etc.. »

Ce but est atteint par la contextualisation des éléments des concepts à transmettre grâce à de courtes animations 3D

mettant en scène les avatars Ludo et Vic qui ont été spécifiquement conçus pour ne stigmatiser aucune population

sur terre et pour promouvoir l’égalité des sexes. Ces saynètes expliquent à l’oral et dans la langue maternelle de

l’apprenant les éléments à transmettre, levant ainsi la barrière de l’écrit et celle de la langue vernaculaire.

Nous avons développé ainsi une application dénommée BasicFrançais, avec un cofinancement européen, qui permet

à des populations allophones d’acquérir les bases du français, initialement au niveau A1.1, et nous nous fixons

comme but d’aller jusqu’au niveau A2.

Notre recherche de « jeunes docteurs en premier CDI » portent sur une application dérivée, nommée BasicX dans

laquelle X est une langue pratiquée sur le territoire français, allant des créoles de Mayotte, à ceux de la Réunion et de

l’arc antillais, aux langues amérindiennes de Guyane, au Kanak de Nouvelle Calédonie, au polynésien, et l’ensemble

des dialectes de la métropole (alsacien(s), basque, picard, occitan(s), etc…), mais aussi les langues parlées par les

migrants. La Direction Générale de la Langue Française et des Langues de France compte environ 75 de ces langues

dialectales, et environ 230 langues sont parlées en Europe.

Le projet de R&D consiste à créer des scénarios d’interaction dans une langue à apprendre, collecter des données et

les analyser, participer au développement des technologies de l’intelligence artificielle dans la langue en question

(reconnaissance, synthèse vocale, gestion des dialogues). Tout en étant ambitieux, ce projet relève du faisable

puisque la plage lexicale du niveau A1 ne comporte qu’environ 1000 mots et une petite centaine de dialogues très

simples.

La personne « idéale » est donc compétente en Traitement Automatique de la Parole et Intelligence Artificielle,

mais maîtrise également un dialecte parlé sur le territoire français, ou une langue issue de l’immigration. Nous

sommes conscients que ce « mouton à cinq pattes » est rare, et considèrerons donc des candidatures venant soit de

la didactique des langues, soit du NLP.

La société est basée en région parisienne, mais les candidats pourront travailler depuis leur lieu habituel de

résidence. Envoyez votre CV à jack@ludo-vic.com

LUDO-VIC SAS – 103 Boulevard Macdonald 75019 PARIS

RCS 824194492 Paris – http://www.ludo-vic.com

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Inria and Vivoka are offering two fully-funded PhD positions in the context of an
academic-industry partnership aiming to further develop the Voice Development Kit (VDK),
the very first solution allowing a company to design an embedded voice interface in a
simple, autonomous and quick way (https://vivoka.com/).

The successful candidates will share their time between Vivoka's R&D team and Inria's
Multispeech team, that is the largest research team in the field of speech processing in
France.

Detailed offers:
- Multi-factor data augmentation and transfer learning for embedded automatic speech
recognition: https://recrutement.inria.fr/public/classic/en/offres/2021-03756
- Joint embedded speech separation, diarization and recognition for the automatic
generation of meeting minutes:
https://recrutement.inria.fr/public/classic/en/offres/2021-03757

Starting date: October 1, 2021
Duration: 3 years
Location: Metz, France
Salary: from 1,870 to 1,950 EUR net/month

To apply:
Submit your application online at the above URLs and send a copy to
recrutement@vivoka.com. Applications will be assessed on a rolling basis. Please apply as
soon as possible and no later than June 25, 2021.

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ASR Research and Development Engineer, Speech

To strengthen our Research and Development (R&D) organization, innovate and

improve our Automatic Speech Recognition (ASR) products , we need

experienced software engineers with specific skills focused on ASR. You will be

working with the ASR research and development team, and the position will be

based in Zurich, Switzerland.

Principal responsibilities

* Work together within ASR R&D team to strengthen and extend the quality and the

functionality of the existing core engine algorithm and framework.

* Document and communicate effectively the design and implementation proposals, and

the intermediate and final development results in team internal meetings, and in wider

R&D or divisional meetings, when requested.

* Define and implement test cases and metrics processes aimed at qualifying the new

developments within the team adopted sw development and testing processes.

* Follow adopted industry standards and agile development models in place, plus be

ready to accommodate rapid customer driven specification changes.

Knowledge, Skills and Qualifications:

Years of Work Experience: 3 years of professional experience are required

Required

Skills:

The successful candidate is a team player and a fast learner with an

analytical mindset and a pragmatic approach to problem solving.

Knowledge of main ASR softwares, DSP theory, feature extraction etc.

Actual experience within ASR research and development teams.

Experience with ASR open source Toolsets such as Kaldi, Sphynx, HTK,

Fairseq, NeMo and other Pytorch / Tensorflow based libraries.

Experience with high level programming languages such as C, C++, Java.

Experience with distributed version control systems (e.g. Git).

Working knowledge of Linux Operating system.

Excellent oral and written communication skills in English.

Preferred

Skills:

Experience with LSTM and/or Attention Neural Networks and other

Deep Learning approaches as applied to ASR domain.

Knowledge of embedded software programming in C/C++.

Experience with continuous integration and delivery processes.

Experience with scripting languages such as Python, Perl, etc.

Experience in software development preferably in embedded/small

resource software system design and development.

Education: Minimum : MSc in computer science, or equivalent

Desirable : PhD degree in Computer Science, Artificial Intelligence,

Machine Learning, Speech Science.

Work Permit: Permit to work in Switzerland (EU-28 or equivalent) required.

Contact: Pierre-Edouard Honnet pe.honnet@telepathy.ai

Vijeta Avijeet vijeta.avijeet@telepathy.ai

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At Radboud University we have a position for a full professor  Artificial Intelligence & Language, Speech and Communication:  https://www.ru.nl/werken-bij/vacature/details-vacature/?recid=1152936&doel=embed&taal=nl

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https://ei.uni-paderborn.de/fileadmin/elektrotechnik/fg/nth/Stellenangebote/Kennziffer4707.pdf

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The Digital Linguistics Lab (head: JProf. Dr.-Ing. Hendrik Buschmeier) at Bielefeld University is seeking to fill a researcher position (PhD-student, E13 TV-L, 100%, fixed-term until 6/2025) in the newly established collaborative research center TRR 318 ?Constructing Explainability?[^1], sub-project A02 ?Monitoring the understanding of explanations?[^2].

Join us to work in a large interdisciplinary team (computer science, linguistics, computational linguistics, psychology, media science, economics and sociology) on research questions in the intersection of explainable AI and human-computer interaction.

Project A02 will carry out interaction studies and build statistical and computational models to monitor explainees' understanding of explanations based on their multimodal feedback (e.g., head nods, facial expressions, gaze, backchannels, clarification requests).

The formal job advertisement with information on how to apply can be found here:

https://uni-bielefeld.hr4you.org/job/view/540/research-position-for-the-sfb-trr-318-subproject-a02-hb?page_lang=en


Questions? Don?t hesitate to get in touch: hbuschme@uni-bielefeld.de

Hendrik Buschmeier


[^1]: https://www.uni-paderborn.de/en/trr318
[^2]: https://www.uni-paderborn.de/en/trr318/subprojects/a02

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