ISCA - International Speech
Communication Association


ISCApad Archive  »  2020  »  ISCApad #269  »  Jobs

ISCApad #269

Thursday, November 12, 2020 by Chris Wellekens

6 Jobs
6-1(2020-05-10) Researcher at GIPSA-Lab Grenoble, France
L'équipe CRISSP (Cognitive Robotics, Interactive Systems & Speech Robotics) du GIPSA-Lab recherche un(e) candidat(e) motivé(e) pour travailler sur la synthèse de parole appliquée à l'interaction face-à-face incarnée. Il(Elle) devra avoir des compétences en apprentissage automatique.
Le travail s'inscrit dans le cadre du projet THERADIA, financé par BPI-France et mené en partenariat avec des laboratoires (EMC, LIG) et des industriels (SBT, ATOS, Pertimm).
 
 
Les candidatures seront examinées de manière continue jusqu'à ce que le poste soit pourvu.
Tous les détails sur les sujets et comment postuler: https://bit.ly/3cW1gy9
 
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6-2(2020-05-11) Tenure-track researcher at CWI, Amsterdam, The Netherlands

We have an open position for a tenure-track researcher at CWI (https://www.cwi.nl/) within our Distributed & Interactive Systems (DIS) group (https://www.dis.cwi.nl/). 
The focus is on Human-Centered Multimedia Systems (https://www.dis.cwi.nl/research-areas/human-centered-multimedia-systems/) and/or Quality of Experience (QoE) in immersive media (https://www.dis.cwi.nl/research-areas/qoe/).

You can find details about the position and application procedure here: 
https://www.cwi.nl/jobs/vacancies/tenure-track-position-in-multimedia-systems-and-human-computer-interaction (application deadline: July 15, 2020)

If you know of any interested candidates looking for such a position, please share with them. They are welcome to get in touch with me  <p.s.cesar@cwi.nl> concerning any questions prior to any formal application process.

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6-3(2020-05-12) Fully-funded 4-year PhD studentships for research in Speech and Language Technologies (SLT) and their Applications , UKRI Centre for doctoral training, 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

** Applications now open for last remaining September 2020 intake places **

Deadline for applications: 31 May 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 is underpinned by a real-world application, directly supported by one of over 30 industry partners. 

  • 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 UK university cities (WhatUni? 2019).


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 strong 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. 

 

We operate a staged admissions process, with application deadlines throughout the year. The final deadline for applications for the remaining places is 31 May 2020. 

 

Applications will be reviewed within 6 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.

 

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.
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6-4(2020-05-20) University assistant position, Johannes Kepler University, Linz, Austria

We are happy to announce a position as a university assistant at the *Institute of
Computational Perception* of the *Johannes Kepler University (JKU) Linz*, Austria:
https://www.jku.at/en/institute-of-computational-perception/.

The position in short: Postdoc level; full-time employment; 2.5 years (start: August
2020, end: January 2023); research and teaching

For more details, please see https://bit.ly/2TrtAAW


At the recently founded *Multimedia Mining and Search Group*, we are searching for a
highly motivated person who holds a *PhD degree in Computer Science or a closely related
discipline* to join our young team. We offer a full-time position, great place to work,
embedding in a great team, flexibility in the choice of research topics, competitive
salary, and social security provisions.

If you are interested and have a strong background in recommender systems, information
retrieval, multimedia, data analytics, machine learning, user modeling, and/or
human-computer interaction, please contact Markus Schedl (markus.schedl@jku.at) and
include the following documents:

* CV (including teaching experience)
* Publication list
* Certificates (including English translations)
* Short statement of your research interests

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6-5(2020-05-26) Fully funded PhD position in data-driven socially assistive robotics,Uppsala University, Sweden

** Fully funded PhD position in data-driven socially assistive robotics**

Uppsala Social Robotics Lab

Department of Information Technology

Uppsala University, Sweden

 

Uppsala University is a comprehensive research-intensive university with a strong international standing. Our mission is to pursue top-quality research and education and to interact constructively with society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has 46.000 students, 7.300 employees and a turnover of SEK 7.3 billion.

The Department of Information Technology holds a leading position in research as well as teaching at all levels. The department has 280 employees, including 120 faculty, 110 PhD students, and 30 research groups. More than 4000 students are enrolled annually.

The Uppsala Social Robotics Lab (https://usr-lab.com) led by Prof. Ginevra Castellano aims to design and develop robots that learn to interact socially with humans and bring benefits to the society we live in, for example in application areas such as education and assistive technology.

 

We are collecting expressions of interest for an upcoming PhD position in data-driven socially assistive robotics for medical applications within a project funded by Uppsala University’s WoMHeR (Women’s Mental Health during the Reproductive lifespan) Centre, in collaboration with the Department of Neuroscience.

 

The PhD project will include the development and evaluation of novel machine learning-based methods for robot-assisted diagnosis of women’s depression around childbirth via automatic analysis of multimodal user behaviour in interactive scenarios.

 

The student will be part of the Uppsala Social Robotics Lab at the Division of Visual Information and Interaction of the Department of Information Technology.

The Uppsala Social Robotics Lab’s focus is on natural interaction with social artefacts such as robots and embodied virtual agents. This domain concerns bringing together multidisciplinary expertise to address new challenges in the area of social robotics, including mutual human-robot co-adaptation, multimodal multiparty natural interaction with social robots, multimodal human affect and social behavior recognition, multimodal expression generation, robot learning from users, behavior personalization, effects of embodiment (physical robot versus embodied virtual agent) and other fundamental aspects of human-robot interaction (HRI). State of the art robots are used, including the Pepper, Nao and Furhat robotic platforms.

 

The position is for four years.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines http://regler.uu.se/?languageId=1.

 

How to send expressions of interest:

To express your interest, you should send to Ginevra Castellano (ginevra.castellano@it.uu.se) by the 10th of June a description of yourself, your research interests, reasons for applying for this particular PhD position and past experience (max. 3 pages), a CV, copies of relevant university degrees and transcripts, links to relevant publications and your MSc thesis (or a summary in case the thesis work is ongoing) and other relevant documents. Candidates are encouraged to provide contact information to up to 3 reference persons. We would also like to know your earliest possible date for starting.

 

Requirements:

Qualifications: The candidates must have an MSc degree in computer science or related areas relevant to the PhD topics. Good programming skills are required and expertise in machine learning appreciated. The PhD position is highly interdisciplinary and requires an understanding and/or interest in psychology and social sciences and willingness to work in an interdisciplinary team.

 

Working in Sweden:

Sweden is a fantastic place for living and working. Swedes are friendly and speak excellent English. The quality of life is high, with a strong emphasis on outdoor activities. The Swedish working climate emphasizes an open atmosphere, with active discussions involving both junior and senior staff. PhD students are full employees, with competitive salaries, pension provision and five weeks of paid leave per year. Spouses of employees are entitled to work permits. Healthcare is free after a small co-pay and the university subsidizes athletic costs, such as a gym membership. The parental benefits in Sweden are among the best in the world, including extensive parental leave (for both parents), paid time off to care for sick children, and affordable daycare. Upon completion of the PhD degree, students are entitled to permanent residency to find employment within Sweden.

 

 

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6-6(2020-06-01) Poste de doctorant financé à l'Université de Grenoble Alpes, France

L'Université Grenoble Alpes recrute un?e doctorant?e (3 ans) entièrement financé?e à
partir d'octobre 2020 pour contribuer au projet GenderedNews.

** Le projet GenderedNews **

Le projet GenderedNews vise à proposer de nouvelles méthodes pour mesurer et expliquer le
niveau de biais de genre dans les médias en France. Ces biais peuvent être définis comme
le fait que les médias d'information tendent d'une part à surpondérer les hommes par
rapport aux femmes en termes de mentions et de citations, et d'autre part à attribuer aux
femmes un rôle social spécifique impliquant souvent, entre autres, l'anonymat, une
capacité d'action réduite dans la société et la confusion entre cette action et leur état
matrimonial ou familial. De nombreuses études empiriques ont prouvé l'existence de ces
biais et ont permis de mieux les comprendre à l'échelle internationale.

Cependant, la recherche sur cette question est souvent basée sur des données limitées en
volume et produites par des ONG, des administrations ou des organismes de réglementation
des médias. Ces données sont généralement traitées par des analyses de contenu manuelles
qui ne permettent pas de rendre compte systématiquement des évolutions des biais sexistes
dans les médias à long terme et sur un grand nombre de sources, ni d'expliquer ces biais
en termes de variables telles que le financement des médias, la taille des salles de
rédaction ou d'autres variables organisationnelles.

Le projet GenderedNews vise à fournir et à analyser des sources de données importantes et
stables dans le temps ainsi qu'à explorer de nouvelles méthodes pour documenter les
préjugés sexistes dans les médias. Il est basé sur un programme de recherche collaboratif
entre un sociologue des médias et un informaticien ayant des compétences en études des
médias, études de genre, traitement du langage naturel et collecte de données numériques.
Il a également une dimension de partenariat importante dans la mesure où plusieurs médias
importants sont associés et fournissent un accès à leurs données.

** Le doctorat : les biais de citation entre hommes et femmes dans l?information **

GenderedNews se concentre sur deux types de biais et deux mesures différentes de ces
biais. Les biais d'échantillonnage se produisent par la sélection d'un échantillon biaisé
de personnes mentionnées dans les médias. Ils peuvent être étudiés en comptant simplement
combien d'hommes et de femmes ont accès à la visibilité publique d'un côté et en étudiant
les modèles de cadrage des hommes et des femmes représentés de l'autre côté. Les biais de
citation proviennent de la sélection d'un échantillon biaisé de personnes qui, en plus
d'être visibles, sont autorisées à exprimer leurs opinions dans les médias. Ils peuvent
également être étudiés en utilisant les deux approches: compter combien d'un côté et
analyser comment de l'autre.

Dans le cadre du projet, le/la doctorant?e contribuera plus spécifiquement à l'étude des
biais de citation. Cela impliquera les tâches et opérations de recherche suivantes:

- Analyse des données : les grands ensembles de données déjà constitués seront utilisés
pour mesurer les biais d'approvisionnement dans l'actualité. Le/la doctorant?e utilisera
notamment l'exploration de texte et les méthodes de NLP pour a) identifier les entités
nommées dans le corpus, b) identifier les modèles de citation des sources dans les textes
et c) étiqueter les entités nommées en fonction de leur sexe (sur la base des prénoms et
des termes spécifiques au genre). Ces tâches permettront ensuite des analyses
multivariées pour expliquer le niveau de diversité de genre atteint dans les différents
médias analysés en fonction de variables sociologiques (par exemple, il sera possible de
mesurer l'effet d'une augmentation de la proportion de femmes dans les rédactions sur le
biais de genre ou l'effet d'une diminution du nombre de journalistes).
- Entretiens : le/la doctorant?e engagé?e poursuivra également une recherche qualitative
sur l?origine des éventuels biais de genre dans les organisations partenaires. Une
première série d'enquêtes permettra de vérifier que les méthodes utilisées pour accéder
au contenu des médias ne biaisent pas les résultats. Des entretiens seront menés avec les
équipes techniques en charge du fonctionnement des API auprès des médias partenaires afin
de mieux comprendre leur fonctionnement et d'identifier d'éventuels biais. Une deuxième
série d'enquêtes se concentrera sur la compréhension de la hiérarchie de l'information
dans ces médias et son influence possible sur les préjugés sexistes. Des entretiens
seront menés avec des journalistes et des rédacteurs en chef pour comprendre ce qui rend
un article d'actualité digne d'intérêt et comment les médias prennent en compte les
questions de genre. Le but de ces interviews sera de comprendre comment les médias en
tant qu'organisation contrôlent les préjugés sexistes dans leurs opérations quotidiennes.
- Sur le plan théorique, cette thèse répondra à certaines questions qui étaient
auparavant laissées dans l'ombre. Il s'agit notamment de la définition et de la mesure
opérationnelle de la « diversité » dans l'actualité en relation avec les questions de
genre pour la communauté des sciences sociales ainsi que la question du biais et de
l'équité dans les algorithmes d'apprentissage automatique pour la communauté NLP.

En plus de la thèse et des publications scientifiques, le/la doctorant?e sera également
impliqué?e dans le projet GenderedNews à différents niveaux:

- Superviser la collecte et le traitement des données avec les médias partenaires;
- Contribuer à la production de nouveaux outils de mesure et de visualisation de la
diversité des contenus et des sources dans les médias;
- Contribuer à la diffusion des résultats auprès du grand public sur un site Internet
servant de plateforme pour promouvoir une approche plus diversifiée de l'actualité.

** Environnement scientifique **

La thèse sera menée en tant que projet de recherche conjoint de l'équipe Régulations du
laboratoire de recherche Pacte (https://www.pacte-grenoble.fr/page/regulations) et de
l'équipe Getalp du laboratoire LIG
(http://www.liglab.fr/en/research/research-areas-and-teams/getalp). Le/la doctorant?e
recruté?e sera principalement hébergé?e au laboratoire de recherche Pacte pour un soutien
quotidien et en partie au LIG.
Le/la doctorant?e bénéficiera également du soutien du projet du Data Institute sur les
médias sociaux et les sciences sociales
(https://data-institute.univ-grenoble-alpes.fr/research/data-science-social-media-and-social- sciences/) et de la Chaire Algorithmic Society du MIAI qui encourage la recherche basée sur la collaboration des sciences sociales et de l?informatique pour contribuer à une meilleure compréhension du fonctionnement des algorithmes et à une évaluation critique des effets de l'IA dans la société
(https://algorithmicsociety.github.io/).

**Comment postuler ?**

Les candidats doivent être titulaires d'un Master en sciences sociales ou en traitement
du langage naturel (ou être sur le point d'en obtenir un). Ils devraient idéalement avoir
une solide expertise dans les méthodes des sciences sociales et une bonne connaissance
des méthodes d'exploration de texte et de traitement du langage. Ils doivent également
avoir une très bonne connaissance des langues française et anglaise pour pouvoir traiter
les données textuelles dans ces deux langues et mener des entretiens principalement en
français.

Les candidatures sont attendues avant le 1er juillet 2020 et doivent être adressées à
Gilles Bastin à gilles.bastin@iepg.fr et François Portet à Francois.Portet@imag.fr.
Les candidat?es doivent joindre :
- une lettre de candidature expliquant pourquoi ils/elles se considèrent capables de
poursuivre ce projet de thèse,
- leur dernier diplôme,
- un CV.
Ils/elles peuvent également ajouter des lettres de recommandation.
Le comité de sélection informera les candidat?es de sa décision avant le 15 juillet 2020.

Si vous avez des questions sur le poste et le projet, veuillez contacter Gilles Bastin et
/ ou François Portet.

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6-7(2020-06-10) 2 post-docs positions at UTDallas, Texas, USA

POST-DOCTORAL POSITION #1

Center for Robust Speech Systems: Robust Speech Technologies Lab 

 

Developing robust speech and language technologies (SLT) for naturalistic audio is the most challenging topic in the broader class of machine learning problems. CRSS-RSTL stands at the forefront of this initiative by making available the largest (150,000 hours) publicly available naturalistic corpus in the world. The FEARLESS STEPS corpus is the collection of multi-speaker time synchronized multi-channel audio from all of NASA’s 12 Apollo Manned Missions. Deployment of such ambitious corpora requires development of state-of-the-art support infrastructure using multiple technologies working synchronously to provide meaningful information to researchers from the science, technology, historical archives, and educational communities. To this end, we are seeking a post-doctoral researcher in the area of speech and language processing and machine learning. The researcher will collaboratively aid in the development of speech, natural language, and spoken dialog systems for noisy multi-channel audio streams. Overseeing digitization of analog tapes, community outreach and engagement, and assisting in cutting edge SLT research are also important tasks for the project.

Those interested should send an email with their resume and areas of interest to John.Hansen@utdallas.edu. More information can be found on our website: CRSS–RSTLab (Robust Speech Technologies Lab) at https://crss.utdallas.edu/

 

 

POST-DOCTORAL POSITION #2

Center for Robust Speech Systems: Cochlear Implant Processing Lab 

 

Cochlear implants are one of the most successful solutions of replacing hearing sensation via an electronic device. However, the search for better sound coding and electrical stimulation strategies could be significantly accelerated by developing a flexible, powerful, portable speech processor for cochlear implants compatible with current smartphones/tablets. We are developing CCi-MOBILE, the next generation of such a research platform, one that will be more flexible and computationally powerful than clinical research devices that will enable implementation and long-term evaluation of advanced signal processing algorithms in naturalistic and diverse acoustic environments. To this end, we are seeking a post-doctoral researcher in the area of cochlear implant signal processing and embedded hardware/systems design. The researcher will collaboratively aid in the development of an embedded (FPGA-based) hardware (PCBs) for speech processing applications. Firmware development in Verilog and Java (Android) for DSP algorithms implementation is also an important task for the project.

Those interested should send an email with their resume and areas of interest to John.Hansen@utdallas.edu. More information can be found on our website: CRSS–CILab (Cochlear Implant Processing Lab) at https://crss.utdallas.edu/CILab/

 

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6-8(2020-06-13) Tenure track researcher at CWI, Amsterdam, The Netherlands

Do you want to work with us at CWI in Amsterdam?

We have an open position for a tenure-track researcher at CWI (https://www.cwi.nl/) within our Distributed & Interactive Systems (DIS) group (https://www.dis.cwi.nl/). 
The focus is on Human-Centered Multimedia Systems (https://www.dis.cwi.nl/research-areas/human-centered-multimedia-systems/) and/or Quality of Experience (QoE) in immersive media (https://www.dis.cwi.nl/research-areas/qoe/).

You can find details about the position and application procedure here: 
https://www.cwi.nl/jobs/vacancies/tenure-track-position-in-multimedia-systems-and-human-computer-interaction (application deadline: July 15, 2020)

If you know of any interested candidates looking for such a position, please share with them. They are welcome to get in touch with me  <p.s.cesar@cwi.nl> concerning any questions prior to any formal application process.

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6-9(2020-06-18) Assistant/Postdoc level a TUWien, Austria

Are you interested to join a vibrant research environment in the center of Europe, as a
Postdoc researcher or PhD student researcher, and work on exciting topics related to
machine learning, recommender systems, or (multimedia) information retrieval?

If so, please have a look at the following two announcements with upcoming deadlines:

* University Assistant/Postdoc level at JKU (40h/week, Aug 2020 - Jan 2023; application
deadline: June 24, 2020):
https://www.jku.at/fileadmin/gruppen/80/Stellenausschreibungen_E/4176_Homepage_E_20.05.2020.pdf

* PhD student researcher at JKU and TU Wien (40h/week, 3 years; application deadline:
July 9, 2020): https://tiss.tuwien.ac.at/mbl/blatt_struktur/anzeigen/10410#p242.3

For more details or informal inquiries, please contact me via markus.schedl@jku.at.

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6-10(2020-06-20) Two fully-funded PhD studentships in automatic speech recognition - University of Sheffield

Subject: Two fully-funded PhD studentships in automatic speech recognition - University of Sheffield 

We are delighted to be able to offer two fully-funded PhD studentships in Automatic Speech Recognition at the Voicebase Centre of the University of Sheffield, to start in October 2020. The Voicebase studentship covers all fees and maintenance for 3 years at standard UK rates.

Topic 1: Semi-supervised Learning for Automatic Speech Recognition

To apply: https://www.jobs.ac.uk/job/CAJ398/phd-studentship-in-semi-supervised-learning-for-automatic-speech-recognition

Deadline: July 24, 2020

Topic 2: Multilingual Speech Recognition

To apply: https://www.jobs.ac.uk/job/CAJ394/phd-studentship-in-multilingual-speech-recognition

Deadline: July 24, 2020

For further information please contact Prof. Thomas Hain (t.hain@sheffield.ac.uk). 

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6-11(2020-06-22) PhD grant, Université de Toulouse, France

Subject: “MOTRYLANG – The role of motor rhythm in language development and

language disorders”

Supervisors: Corine Astésano, Jessica Tallet

Host Laboratories:

U.R.I Octogone-Lordat (EA 4156), Université de Toulouse II

Laboratoire ToNIC (UMR 1214), Université Paul Sabatier - Toulouse III

Discipline: Linguistics

Doctoral School: Comportement, Langage, Education, Socialisation, Cognition (CLESCO)

Scientific description of the research project:

The project aims to address a series of scientific and clinical questions regarding the place of

motor activity in child language development and its rehabilitation.

Typical development comes with the implementation of rhythm in speech production

(prosodic accenting) and also in movement production (tapping, walking, sensorimotor

synchronisation…). Interestingly, the tempo of linguistic and motor rhythms is similar in

healthy adults (around 700 ms or 1,4 Hz).

The present project aims to (1) investigate the existence of a link between motor & linguistic

rhythms and associated neural correlates (ElectroEncephaloGraphy) in children with and

without linguistic disorders; (2) evaluate the impact of motor training on linguistic

performances, and (3) create, computerize and test a language rehabilitation program based on

the use of motor rhythm in children with language acquisition disorders.

This project will have scientific repercussions in linguistic and movement sciences as well as

in the field of rehabilitation.

The selected candidate will benefit from a stimulating scientific environment: (s)he will

integrate the Interdisciplinary Research Unit Octogone-Lordat (Toulouse II:

http://octogone.univ-tlse2.fr/) and will be co-supervised by Corine Astésano, linguistphonetician

specializing in prosody, and by Jessica Tallet, specialist in rhythmic motor skills

and learning at ToNIC laboratory, Toulouse NeuroImaging Center (Toulouse III:

https://tonic.inserm.fr/). The research will be integrated in a work group on Language,

Rhythm and Motor skills, which encompasses PhD students, professionals in rehabilitation

and collaborators from other universities.

Required skills:

- Master in linguistics, human movement sciences, cognitive sciences, health sciences

or equivalent

- A speech therapist’s profile would be a plus

- Experience in experimental phonetics and/or linguistics, neuro-psycho-linguistics

(speech disorders)

- Skills in linguistic data processing and analysis

- Skills in evaluating speech neurological disorders and in the running of linguistic

remediation programs

- Autonomy and motivation for learning new skills (for eg. EEG …)

- Good knowledge of the French language; good writing and oral skills in both French

and English

Salary:

- 1768.55 monthly gross, 3 year contract

Calendar:

- Sending of applications: 6th july 2020

- Audition of selected candidates: 15th july 2020

- Start of contract: 1rst october 2020

Applications must be sent to Corine Astésano (corine.astesano at univ-tlse2.fr) and will

include:

- A detailed CV, with list of publications if applicable

- A copy of the grades for the Master’s degree

- A summary of the Master’s dissertation and a pdf file of the Master’s dissertation

- A cover letter / letter of interest and/or scientific project (1 page max)

- A letter of recommendation from a referent scientific personality/supervisor

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6-12(2020-06-30) PhD at Université Grenoble Alpes, France

L'Université Grenoble Alpes recrute une doctorante ou un doctorant avec
un contrat de 3 ans entièrement financé à partir d'octobre 2020 dans le
cadre du projet THERADIA.


*Le projet THERADIA*

Le projet THERADIA consiste à mettre au point un assistant thérapeutique
virtuel qui constitue le relais et l?interface entre le patient et le
thérapeute mais aussi les aidants qui gravitent autour du patient. Par
un usage massif de différentes technologies d?intelligence artificielle,
cet assistant empathique sera chargé d?adapter le traitement aux besoins
du patient sous le contrôle du thérapeute, et de s?assurer de son suivi
en interagissant avec les différents acteurs (thérapeute, patient,
aidants) selon un mode conversationnel. Par rapport à un bot classique,
l?objectif est de doter l?assistant d?une intelligence artificielle
affective basée sur l?analyse des échanges verbaux et non verbaux
(parole, prosodie et expressions du visage). L?assistant devra également
être capable de synthétiser différents styles comportementaux pour
interagir efficacement, et de résumer le fil d?interactions avec le
patient pour restituer au thérapeute ou aux aidants un résumé des
échanges et progrès réalisés. Validé d?un point de vue médico-économique
dans le contexte de THERADIA, ces technologies pourront trouver de
nombreux autres débouchés pour assister des humains amenés à
sous-traiter à une intelligence artificielle les dimensions affectives
de leurs interactions.


*Sujet de thèse* : Apprentissage profond et hybride pour la génération
automatique de résumés multimédia d?observance de remédiation cognitive
pour les aidants et cliniciens.

L?objectif de cette thèse de doctorat est de concevoir des méthodes
permettant de générer un compte rendu pertinent pour les aidants et les
orthophonistes synthétisant la prise en charge de patients souffrants de
troubles cognitifs lors de séances de remédiations cognitives réalisées
à domicile. La tâche de génération est donc de concevoir un système
capable :
- d'identifier, d'agréger, de sélectionner et de structurer les
informations pertinentes à communiquer au destinataire
- de transformer ces informations structurées en un document multimédia
cohérent
- d'adapter la réalisation vis-à-vis des critères de génération (type de
destinataire, période à résumer, taille du texte)

Afin de gérer la grande complexité de la tâche deux approches
complémentaires seront étudiées. Une approche experte (génération guidée
par les experts médicaux) [Portet et al. 2009] et une approche neuronale
de bout-en-bout apprise par renforcement (génération guidée par les
lecteurs) [Brenon et al. 2018]. Une approche hybride sera également
étudiée [Li et al. 2019].  Ces approches sont dépendantes d'un ensemble
d'exemples disponibles pour induire le fonctionnement de ces différents
choix. Malheureusement, ces données ne sont pas disponibles aujourd'hui.
Basé sur nos derniers travaux, nous aborderons ce problème avec un
modèle neuronal dans un cadre d'apprentissage faiblement supervisé qui
est capable de tirer parti d'un faible nombre de données supervisées et
d'un grand nombre de données non-supervisées. Dans notre cas les données
supervisées seront celles acquises dans le projet et les données non
supervisées seront obtenues auprès des partenaires (rapport d?entretien)
et sur le web (textes comportant des émotions). L'état de l'art montre
que des avancées importantes ont été faites en génération automatique de
textes avec des modèles neuronaux. Cependant, ces avancées se sont
appuyées sur des corpus de grande taille et sur des tâches isolées et
bien définies. Dans notre cas, la difficulté porte principalement sur la
sélection des informations en fonction du profil du destinataire, le
manque de corpus adéquat et de mesure de performances. Un des objectifs
de cette thèse sera de mettre en ?uvre des méthodes efficaces
d'apprentissage faiblement supervisé [Qader et al. 2019], de
renforcement et de collecte de données auprès des utilisateurs cibles du
projet.

Enfin, l'évaluation de la génération automatique de textes est une tâche
complexe. En effet, plusieurs dimensions linguistiques, sémantiques et
applicatives doivent être prises en compte (grammaire, cohérence,
émotion, pertinence, utilité). La qualité des sorties textuelles pourra
être évaluée, d'une part, par des mesures automatiques sur corpus lors
du développement de l'approche et, d'autre part, par des évaluations
subjectives humaines (patients, aidants, experts) sur chaque itération
majeure du système de génération.


*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. 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).
La personne sera également amenée à collaborer avec plusieurs équipes
impliquées dans le projet THERADIA, en particulier avec des chercheurs
du GIPSA-lab situé également à Grenoble et les collaborateurs de
l?entreprise SBT HumanMatter(s) basée à Lyon.


*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
en évaluation impliquant l?humain. Ils doivent également avoir une très
bonne connaissance des langues française et anglaise pour pouvoir
traiter les données textuelles dans ces deux langues et mener des
entretiens principalement en français. Une expérience dans le domaine de
la génération automatique de textes serait un plus.

Les candidatures sont attendues au fil de l?eau et le poste sera ouvert
jusqu?à ce qu?il soit pourvu. Elles doivent contenir : CV + lettre/message
de motivation + notes master + lettre(s) de recommandations; et être
adressées à François Portet (Francois.Portet@imag.fr) et Fabien Ringeval
(Fabien.Ringeval@imag.fr).


*References*
Brenon A., Portet F., Vacher M. (2018) Arcades : A deep model for
adaptive decision making in voice controlled smart-homes. Pervasive and
Mobile Computing, 49, pp.92-110
Li Y., Liang X., Hu Z. et al. (2018) Hybrid retrieval-generation
reinforced agent for medical image report generation. Advances in Neural
Information Processing Systems. p. 1530-1540.
Portet F., Reiter E., Gatt A., Hunter J., Sripada S., Freer Y., Sykes C.
(2009) Automatic generation of textual summaries from neonatal intensive
care data. Artificial Intelligence, 173 (7-8), pp.789-816.
Qader R., Portet F., Labbé C. (2019) Neural Text Generation from
Unannotated Data by Joint Learning of Natural Language Generation and
Natural Language Understanding Models, INLG2019


--
François PORTET
Maître de conférences - Grenoble Institute of Technology
Laboratoire d'Informatique de Grenoble - Équipe GETALP
Bâtiment IMAG - Office 331
700 avenue Centrale
Domaine Universitaire - 38401 St Martin d'Hères
FRANCE

Phone:  +33 (0)4 57 42 15 44
Email:  francois.portet@imag.fr
www:    http://membres-liglab.imag.fr/portet/

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6-13(2020-07-09) Speech Research Scientist at ETS R&D

Speech Research Scientist at ETS R&D:

 

https://etscareers.pereless.com/index.cfm?fuseaction=83080.viewjobdetail&CID=83080&JID=302819

 

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6-14(2020-07-16) Early Stage Researcher / PhD student in an EU Marie Sklodowska-Curie Action (H2020-MSCA-ITN), Romania

We are hiring an Early Stage Researcher / PhD student in an EU Marie Sklodowska-Curie Action (H2020-MSCA-ITN) on the topic 'Designing and Engineering Multimodal Feedback to Augment the User Experience of Touch Input' under the supervision of Prof. Radu-Daniel Vatavu (http://www.eed.usv.ro/~vatavu)

The job will take place in the Machine Intelligence and Information Visualization Laboratory at Stefan cel Mare University of Suceava, Romania. The position is full-time (40 hours/week) for a fixed term of 3 years starting September 28, 2020.

Salary starts at 2849.76? per month (gross amount) and the research will be conducted in a network of EU partner universities. Full details here: http://www.eed.usv.ro/mintviz/jobs/Job-Announcement-ESR860114.pdf

Other information:
https://cordis.europa.eu/project/rcn/224423/en
https://euraxess.ec.europa.eu/jobs/491894
http://multitouch.fgiraud.ovh/wpjb-jobs/phd-multimodal-feedback/  

**** Deadline: September 1, 2020 ****

Any inquiries about this position are welcome at any time at the email address radu.vatavu@usm.ro with the subject 'ESR MULTITOUCH-860114'

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6-15(2020-07-20) Technical project manager, INRIA, Nancy, France

Inria is seeking a Technical Project Manager for a European (H2020 ICT) collaborative
project called COMPRISE (https://www.compriseh2020.eu/).

COMPRISE is a 3-year Research and Innovation Action (RIA) aiming at new cost-effective,
multilingual, privacy-driven voice interaction technology. This will be achieved through
research advances in privacy-driven deep learning, personalized training, automatic data
labeling, and tighter integration of speech and dialog processing with machine
translation. The consortium includes academic and industrial partners in France, Germany,
Latvia, and Spain. The project has been ongoing for 1.5 year, and it has received very
positive feedback at its first review.

The successful candidate will be part of the Multispeech team at Inria Nancy (France). As
the Technical Project Manager of H2020 COMPRISE, he/she will be responsible for animating
the consortium in daily collaboration with the project lead. This includes orchestrating
scientific and technical collaborations as well as reporting, disseminating, and
communicating the results. He/she will also lead Inria?s software development and
demonstration tasks.

Besides the management of COMPRISE, the successful candidate will devote half of his/her
time to other activities relevant to Inria. Depending on his/her expertise and wishes,
these may include: management of R&D projects in other fields of computer science,
involvement in software and technology development and demonstration tasks, building of
industry relationships, participation in the setup of academic-industry collaborations,
support with drafting and proofreading new project proposals, etc.

Ideal profile:
- MSc or PhD in speech and language processing, machine learning, or a related field
- at least 5 years' experience after MSc/PhD, ideally in the private sector
- excellent software engineering, project management, and communication skills

Application deadline: August 30, 2020

Starting date: October 1, 2020
Duration: 14 months
Location: Nancy, France
Salary: from 2,300 to 3,700 EUR net/month, according to experience

For more details and to apply:
https://jobs.inria.fr/public/classic/en/offres/2020-02908

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6-16(2020-09-10) Experts in recognition and synthesis at Reykjavík University's Language and Voice Lab, Iceland
Reykjavík University's Language and Voice Lab (https://lvl.ru.is) is looking 
for experts in speech recognition and in speech synthesis. At the LVL you 
will be joining a research team working on exciting developments in language
 technology as a part of the Icelandic Language Technology Programme 
(https://arxiv.org/pdf/2003.09244.pdf). 
Job Duties:
 . Conduct independent research in the fields of speech processing, 
machine learning, speech recognition/synthesis and human-computer interaction.
 . Work with a team of other experts in carrying out the Speech 
Recognition/Synthesis part of the Icelandic Language Technology Programme. 
. Publish and disseminate research findings in journals and present at 
conferences. . Actively take part in scientific and industrial cooperation projects. 
. Assist in supervising Bachelor's/Master's students. Skills: 
. MSc/PhD degree in engineering, computer science, statistics, mathematics 
or similar
. Good programming skills (e.g. C++ and Python) and knowledge of Linux 
(necessary). 
. Good knowledge of a deep learning library such as PyTorch or TensorFlow
 (necessary). 
. Good knowledge of KALDI (preferable)
. Background in language technology (preferable).
 . Good skills in writing and understanding shell scripts (preferable).
 
 All applications must be accompanied by a good CV with information about 
previous jobs, education, references etc. It is also optional to attach a cover 
letter where the applicant can justify the reasons for being the right person
 for the job. Here is the link to apply: https://jobs.50skills.com/ru/is/5484
Applications deadline is October 4th 2020.
 Applications are only accepted through RU's recruitment system. 
All inquiries and applications will be treated as confidential. 
Further information about the job is provided by Jón Guðnason 
Associate Professor, jg@ru.is, and Ester Gústavsdóttir, Director of Human 
Resources, esterg@ru.is. 
 
The role of Reykjavik University is to create and disseminate knowledge to 
enhance the competitiveness and quality of life for individuals and society, 
guided by good ethics, sustainability and responsibility. 
Education and research at RU are based on strong ties with industry and society.
We emphasize interdisciplinary collaboration, international relations and 
entrepreneurship. 
 
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6-17(2020-09-17) Proposition de contrat doctoral, Sorbonne University (Jussieu), Paris, France

Proposition de contrat doctoral

Titre :

Rythme de la parole et gestes manuels en synthèse performative

Résumé du sujet :

Le but de cette thèse est de développer un cadre théorique et des expérimentations quant à

l'utilisation du geste manuel pour le contrôle prosodique via des interfaces humain-machine, en

synthèse performative. La synthèse vocale performative est un nouveau paradigme de recherche en

interaction humain-machine, dans lequel une voix de synthèse est jouée comme un instrument en

temps-réel à l’aide des membres (mains, pieds). Le contrôle du rythme de parole par les mains est

un problème qui implique des unités rythmiques, des points de contrôle rythmique, les centres

perceptifs des syllabes et des gestes de tapotement (tapping), voire des partitions gestuelles

inspirées des phonologies autosegmentales ou articulatoires. Les unités rythmiques varient en

fonction de la phonologie de la langue étudiée, ici le français, l’anglais et le chinois mandarin. Les

enjeux de la thèse portent donc sur la modélisation des schémas de perception-action impliqués

dans le contrôle rythmique, la modélisation des unités temporelles, la réalisation et l’évaluation

d’un système de contrôle du rythme. Les applications visées sont :

1. l’apprentissage du contrôle naturel des contours intonatifs à l'aide de la chironomie pour

l'acquisition de langues étrangères (anglais, français, mandarin) ;

2. l’apprentissage du contrôle chironomique des contours d'intonation de la langue maternelle,

pour la suppléance vocale (larynx artificiel).

Contexte :

La voix n’est pas un 'instrument' de musique, au sens d’un artefact mis en vibration par les

membres ou par le souffle. Les organes vocaux sont internes, en grande partie invisibles, et

contrôlés de façon complexe par plusieurs ensembles musculaires (respiration, phonation,

articulation). Le contrôle vocal est donc, par nature intéroceptif, alors qu’il est davantage

kinesthésique et extéroceptif pour les instruments de musique.

L’avènement de la synthèse numérique permet pour la première le rendu d’un son indéniablement

vocal par un dispositif instrumental externe, mis à distance de l’appareil vocal. Les 'instruments

vocaux' sont 'manoeuvrés' par les mains, les pieds, à l’aide de capteurs ou d’interfaces humainmachine.

Cette mise à distance pose la question du contrôle vocal dans des termes tout à fait

différents de ceux du contrôle d’un instrument acoustique ou de la voix elle même. Les instruments

vocaux permettent actuellement un contrôle musical de la phonation : intonation, séquencement

rythmique, qualité de voix, pour la voix chantée. Le contrôle très précis de l’articulation et du

rythme en parole est encore problématique. Le propos de cette thèse est de traiter la question du

contrôle gestuel du rythme prosodique et du séquencement articulatoire.

Objectifs et résultats attendus :

Cette thèse s’inscrit dans la ligne de recherche sur les instruments vocaux. Un instrument

vocal est un synthétiseur en vocal temps réel à contrôle gestuel. La synthèse est réalisée par un

programme pour produire les échantillons. Le contrôle gestuel utilise des interfaces pour capter les

gestes. Les mouvements des articulateurs étant très rapides, il est difficile de les contrôler de façon

directe par les gestes manuels et des méthodologies basées sur la représentation phonologique du

rythme prosodique doivent être mise en place.

Le rythme est réalisé par des gestes des membres, mains ou pieds, en place des gestes articulatoire

qui correspondent aux syllabes. Les circuits de perception-action ne sont plus les mêmes, ni les

vélocités des organes mis en mouvement. Le contrôle du rythme prosodique en synthèse

performative est donc un problème qui implique la définition d’unités rythmiques, de points de

contrôle rythmique, de centres perceptifs des syllabes, de gestes de tapotement (tapping), voire de

partitions gestuelles inspirées des phonologies autosegmentale ou articulatoire.

Des points de contrôles rythmiques doivent enrichir le signal vocal pour permettre d’en manipuler

le déroulement temporel. Ces points doivent avoir du sens du point de vue de la phonologie de la

langue jouée, et de sa phonotactique. La perception du flux syllabique, avec ses centres perceptifs,

est donc impliquée. Les gestes de contrôle, par appuis ou tapotage, impliquent des processus

moteurs, à la fois analogues et différents de ceux des articulateurs. Les unités rythmiques varient en

fonction de la phonologie des langues étudiées, ici le français, l’anglais et le chinois mandarin. Les

enjeux de la thèse portent donc sur la modélisation des schémas de perception-action impliqués

dans le contrôle rythmique, la modélisation des unités temporelles, la réalisation et l’évaluation

d’un système de contrôle du rythme.

Les résultats attendus sont à la fois théoriques et pratiques :

L’expérimentation perceptive permettra de mettre en relation les différentes unités

temporelles;

les théories phonologiques sur l’organisation du geste phonatoire seront mises à l’épreuve

avec un nouveau paradigme expérimental;

un nouveau synthétiseur sera réalisé;

un ensemble de méthodes pour le contrôle gestuel de la synthèse, de nouveaux gestes et des

interfaces adaptées seront développés et testés dans les tâches applicative visées, soit

l’apprentissage du contrôle naturel des contours intonatifs à l'aide de la chironomie pour

l'acquisition de langues étrangères (anglais, français, mandarin) et l’apprentissage du

contrôle chironomique des contours d'intonation de la langue maternelle, pour la suppléance

vocale (larynx artificiel).

Méthodologie :

Les théories phonologiques et phonétiques de l’organisation temporelle des langues étudiées seront

considérées dans le contexte du paradigme de la synthèse performative. L’étude des relations entre

points de contrôle rythmique, centres perceptifs, gestes de tapotage et unités phonologiques

implique la modélisation et l’expérimentation, avec des sujets réalisant des tâches de perceptionaction.

La méthodologie relève ici de la psychologie et de la phonétique expérimentales : définition

de corpus, mise en oeuvre de protocoles de test, tests, analyses statistiques.

Un synthétiseur qui utilise les nouveaux paradigmes de contrôle rythmique sera développé. La

méthodologie relève ici du traitement du signal audio et de la parole ainsi que de l’informatique,

depuis la conception jusqu’à la programmation.

Ainsi un ensemble de méthodes pour le contrôle gestuel du rythme prosodique et du temps sera

développé et testé dans les tâches applicatives visées. Ces méthodes comprennent à la fois les gestes

et les interfaces de contrôle et relèvent de l’informatique dans le domaine des interfaces humainmachine.

Prérequis :

Ce sujet est à l’interface de la synthèse vocale et des interfaces humain-machine, de la prosodie, de

la perception et de la performance musicale. Cela demande des connaissances générales en

traitement du signal audionumérique et en informatique musicale ou en interface humain-machine.

Une partie du travail portera sur le développement logiciel. Des connaissances sur la voix et la

parole, en phonétique et phonologie, ainsi qu’en psychologie expérimentale ou sciences cognitives

seront nécessaires.

Les candidatures avec une formation initiale en informatique et traitement du signal aussi bien que

celles avec une formation initiale en linguistique, phonétique ou sciences cognitives seront

considérées. La formation initiale sera éventuellement complétée dans les domaines qui seraient

moins connus.

Encadrement :

Christophe d’Alessandro, DR CNRS, Responsable de l’équipe LAM

Institut Jean Le Rond d’Alembert, Sorbonne Université

christophe.dalessandro@sorbonne-universite.fr

Ce projet doctoral est dans le cadre du contrat ANR Gepeto, en collaboration avec le LPP

(Sorbonne nouvelle), et le GIPSA-Lab, Université de Grenoble.

Début du contrat dès que possible (à partir d’octobre 2020)

Références :

Delalez, S. et d’Alessandro, C. (2017). “Vokinesis: syllabic control points for performative

singing synthesis”, NIME’17 , pp. 198-203.

X. Xiao, G. Locqueville, C. d'Alessandro, B. Doval, « T-Voks: Controlling Singing and

Speaking Synthesis with the Theremin », Proceedings of the International Conference on

New Interfaces for Musical Expression, NIME’19, June 3-6, 2019, Porto Alegre, Brazil,

110-115.

Samuel Delalez, Christophe d’Alessandro « Adjusting the Frame: Biphasic Performative

Control of Speech Rhythm », Proc. INTERSPEECH 2017, 18th Annual Conference of the

International Speech Communication Association, Stockholm, Sweden, August 18-25,

2017, DOI: 10.21437/Interspeech.2017, 864-868.

Christophe d’Alessandro, Albert Rilliard, and Sylvain Le Beux « Chironomic stylization of

intonation » J. Acoust. Soc. Am., 129(3), march 2011, 1594-1604

Christophe d’Alessandro, Lionel Feugère, Sylvain Le Beux, Olivier Perrotin, and Albert

Rilliard (2014) , « Drawing melodies : evaluation of chironomic singing synthesis » , J.

Acoust. Soc. Am. 135 (6), 3601-3612.

I . Chow, M. Belyk, V. Tran, and S. Brown. Syllable synchronisation and the P-center in

cantonese. 49 :55–66, 2015.

C. d’Alessandro, L. Feugère, S. Le Beux, and O. Perrotin. Drawing melodies : Evaluation of

chironomic singing synthesis. J. Acoust. Soc. Am., 135(6) :3601–3612, March 2014.

C. d’Alessandro, A. Rilliard, and S. Le Beux. Chironomic stylisation of intonation. J.

Acoust. Soc. Am., 129(3) :1594–1604, March 2011.

C. Fowler. “Perceptual centers” in speech production and perception. Perception &

Psychophysics, 25 :375–388, 1979.

P. Howell. Predicton of p-center location from the distribution of energy in the ampitude

envelope. Perception and Psychophysics, 43 :90–93, 1988.

P. F. MacNeilage. The frame/content theory of evolution of speech production. Behavioral

and Brain Sciences, 21 :499–546, 1998.

S.M. Marcus. Acoustic determinants of perceptual center (P-center). Perception and

Psychophysics, 30 :247–256, 1981.

J. Morton, S. Marcus, and C. Frankish. Perceptual centers (P-Centers). Psychological

Review, 83(5) :405–408, 1976.

B. Pompino-Marshall. On the psycho-acoustic nature of the p-center phenomenon. Journal

of phonetics, 17 :175–192, 1989.

K. Rapp-Holmgren. A study of syllable timing. STL-QPSR, 12(1) :014–019, 1971.

B. H. Repp. Sensorimtor synchronization : A review of tapping litterature. Psychon. Bull.

Rev., 12(6) :969–992, 2005.

B. H. Repp and Y. H. Su. Sensorimotor synchronisation : A review of recent research.

Pyschon. Bull. Rev., 20 :403–452, 2013.

P. Wagner. The Rhythm of Language and Speech : Constraining Factors, Models, Metrics

and Applications. Habilitation à diriger des recherches, Rheinischen Friedrich-Wilhelms-

Universität Bonn, 2008.

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6-18(2020-09-24) Offre de thèse: Modèles profonds pour la reconnaissance et l'analyse de la parole spontanée,Grenoble, France

*Sujet*
Modèles profonds pour la reconnaissance et l'analyse de la parole spontanée

Le traitement de la parole spontanée est l'un des défis majeurs que doit relever le
domaine de la reconnaissance automatique de la Parole (RAP). La parole spontanée est
significativement différente de la parole préparée (lecture, film, discours
radiophonique, commande vocale, etc) notamment à cause des disfluences (pause,
répétition, réparation, faux départ). Ces caractéristiques mettent en défaut les systèmes
de RAP traditionnels car la structure de la parole spontanée est beaucoup plus difficile
à modéliser que celle de la parole préparée.

Dans ce projet de doctorat nous nous intéresseront aux méthodes sans lexique basées sur
des architectures de séquence à séquence pour, dans un premier volet,  améliorer les
performances de la RAP sur la parole spontanée et, dans un deuxième volet, étudier les
structures internes des modèles neuronaux pour faire émerger de nouvelles hypothèses sur
la parole spontanée.
Dans le premier volet, les modèles séquence à séquence seront conçu et appris en
s?appuyant sur les corpus transcrits existants (plus de 300 heures) enregistrés dans la
communication quotidienne (enregistrements de discours au sein d'une famille, dans un
magasin, lors d'un entretien, etc.) pour apprendre des modèles profonds de la parole
spontanée.

Le deuxième volet de la thèse consistera à analyser les représentations apprises par les
modèles pour les confronter aux théories et modèles linguistiques sur la parole spontanée
au niveau prosodique, phonétique et grammatical. Les contributions de la thèse seront de
produire des systèmes de reconnaissance de parole adapté à la parole spontanée, de
permettre d'expliquer ces modèles par rapport à la connaissance actuelle sur la parole
spontanée, de faire ressortir des caractéristiques linguistiques intrinsèques à la parole
spontanée.

La thèse s'effectuera en collaboration avec des enseignants chercheurs du Lidilem pour
des applications en socio-linguistique et linguistique de terrain.

*Environnement scientifique*
La thèse sera menée au sein de l'équipe Getalp du laboratoire LIG (http: // www.
liglab.fr/en/research/research-areas-and-teams/getalp) en collaboration avec le
Laboratoire LIDILEM (https://lidilem.univ-grenoble-alpes.fr/). Le/la doctorant?e
recruté?e bénéficiera également du soutien de la Chaire Artificial Intelligence &
Language de l'intitut MIAI (https://miai.univ-grenoble-alpes.fr).

*Comment postuler ?*
Les candidats doivent être titulaires d'un Master en informatique ou en traitement du
langage naturel. Ils devraient idéalement avoir une
solide expertise en traitement automatique de la parole. Ils doivent également avoir une
très bonne connaissance des langues française et anglaise. Les candidatures sont
attendues avant le 9 octobre 2020 et doivent être adressées à François Portet à
Francois.Portet@imag.fr et Solange Rossato Solange.Rossato@imag.fr
Les candidat?es doivent joindre :
- une lettre de candidature expliquant pourquoi ils/elles se considèrent capables de
poursuivre ce projet de thèse,
- leur dernier diplôme,
- un CV.
Ils/elles peuvent également ajouter des lettres de recommandation.

Le comité de sélection informera les candidat?es de sa décision avant le 15 octobre 2020.
Si vous avez d'autres questions sur le poste et le projet, veuillez contacter François
Portet et/ou Solange Rossato

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6-19é2020-10-05) Research Assistant/Associate in Spoken Language Processing, Cambridge University, UK
Research Assistant/Associate in Spoken Language Processing x 2 (Fixed Term)
Speech Research Group, Cambridge University Engineering Department, UK
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6-20(2020-10-05) 2 POsitions at Radbout University, Nijmegen, The Netherlands

 

We have two vacancies for speech technology employees:

 

 

 

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6-21(2020-10-08) Job offer: 1-year postdoc position at LSP (ENS Paris), France

Job offer: 1-year postdoc position at LSP (with a possibility of 1-year extension)

The Laboratoire des Systèmes Perceptifs (LSP, ENS Paris / CNRS, https://lsp.dec.ens.fr/en) is offering a postdoc position for the ANR project fastACI ('Exploring phoneme representations and their adaptability using fast Auditory Classification Images') supervised by Léo Varnet (leo.varnet@ens.psl.eu).

The fastACI project aims to develop a fast and robust experimental method to visualize and characterize the auditory mechanisms involved in phoneme recognition. The fast-ACI method relies on a stimulus-response model, combined with a reverse correlation (revcorr) experimental paradigm. This approach, producing an 'instant picture' of a participant’s listening strategy in a given context, has already yielded conclusive results, but remains very time-consuming. Therefore, the main objectives of this postdoc contract will be (1) to improve the efficiency of the process using advanced supervised learning techniques (e.g. sparse priors on a smooth basis) and an online adaptive protocol (e.g. Bayesian optimisation); then (2) to use this technique to map the phonemic representations used by normal-hearing listeners. For this purpose, a large number of experiments on phoneme-in-noise categorization tasks (e.g. /aba/ vs. /ada/) will be carried out, in order to insure the broadest possible mapping of the French phonological inventory. As a second step, the new tool will be used to explore the adaptability of speech comprehension in the case of sensorineural hearing loss and noisy backgrounds.

The post-doc will be involved in all activities in line with the project, including data collection, coding and statistical analyses. The post-doc will also coordinate trainees’ and students’ work involved in the project, and contribute significantly to publication of the findings.

Required profile:

Background in psychoacoustics and/or machine learning (the candidate should preferably hold a PhD in one of these fields)

 High skills in statistical data processing (in particular supervised learning algorithms) and practical knowledge in psychophysics

 Basic understanding of psychoacoustics and psycholinguistics

 

 Good knowledge of Matlab programming (other languages such as R can also be useful)

 Strong communication skills in English (working language in the lab) and French (interactions with participants, etc.).

Duration: 12 months or 24 months

Start: Early 2021

Net salary: ~ 2100€/ month

Application Procedure:

Applications must include a detailed CV and a motivation letter, a link to (or copy of) the PhD thesis, PhD Viva report, plus the email contact of 2 referees. Applications are to be sent to: Léo Varnet (leo.varnet@ens.psl.eu) before 08/11 (interviews should take place on the 18/11 by videoconference)

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6-22(2020-10-20) PhD grant at INRIA Nancy, France

Privacy preserving and personalized transformations for speech recognition

 

This research position fits within the scope of a collaborative project (funded by the French National Research Agency) involving several French teams, among which, the MULTISPEECH team of Inria Nancy - Grand-Est.

One objective of the project is to transform speech data in order to hide some speaker characteristics (such as voice identity, gender information, emotion, ...) in order to safely share the transformed data while keeping speaker privacy. The shared data is to be used to train and optimize models for speech recognition.

The selected candidate will collaborate with other members of the project, and will participate to the project meetings.

 

Scientific Context

Over the last decade, great progress has been made in automatic speech recognition [Saon et al., 2017; Xiong et al., 2017]. This is due to the maturity of machine learning techniques (e.g., advanced forms of deep learning), to the availability of very large datasets, and to the increase in computational power. Consequently, the use of speech recognition is now spreading in many applications, such as virtual assistants (as for instance Apple’s Siri, Google Now, Microsoft’s Cortana, or Amazon’s Alexa) which collect, process and store personal speech data in centralized servers, raising serious concerns regarding the privacy of the data of their users. Embedded speech recognition frameworks have recently been introduced to address privacy issues during the recognition phase: in this case, a (pretrained) speech recognition model is shipped to the user's device so that the processing can be done locally without the user sharing its data. However, speech recognition technology still has limited performance in adverse conditions (e.g., noisy environments, reverberated speech, strong accents, etc.) and thus, there is a need for performance improvement. This can only be achieved by using large speech corpora that are representative of the actual users and of the various usage conditions. There is therefore a strong need to share speech data for improved training that is beneficial to all users, while preserving the privacy of the users, which means at least keeping the speaker identity and voice characteristics private [1].

Missions: (objectives, approach, etc.)

Within this context, the objective is twofold. First, it aims at improving privacy preserving transforms of the speech data, and, second, it will investigate the use of additional personalized transforms, that can be applied on the user’s terminal, to increase speech recognition performance.

In the proposed approach, the device of each user will not share its raw speech data, but a privacy preserving transformation of the user speech data. In such approach, some private computations will be handled locally, while some cross-user computations may be carried out on a server using the transformed speech data, which protect the speaker identity and some of his/her features (gender, sentiment, emotions...). More specifically, this rely on a representation learning to separate the features of the user data that can expose private information from generic ones useful for the task of interest, i.e., here, the recognition of the linguistic content. On this topic, recent experiments have relied on Generative Adversarial Networks (GANs) for proposing a privacy preserving transform [Srivastava et al., 2019], and on voice conversion approaches [Srivastava et al., 2020].

In addition, as devices are getting more and more personal, this creates opportunities to make speech recognition more personalized. Some recent studies have investigated approaches that takes benefit of speaker information [Turan et al., 2020].

The candidate will investigate further approaches along these lines. Other topics such as investigating the impact and benefit of adding some random noise in the transforms will be part of the studies, as well as dealing with (hiding) some paralinguistic characteristics. Research directions and priorities will take into account new state-of-the-art results and on-going activities in the project.

 

Skills and profile:

PhD or Master in machine learning or in computer science

Background in statistics, and in deep learning

Experience with deep learning tools is a plus

Good computer skills (preferably in Python)

Experience in speech and/or speaker recognition is a plus

 

Bibliography:

[Saon et al., 2017] G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim, B. Roomi, and P. Hall: English conversational telephone speech recognition by humans and machines. Technical report, arXiv:1703.02136, 2017.

[Srivastava et al., 2019] B. Srivastava, A. Bellet, M. Tommasi, and E. Vincent: Privacy preserving adversarial representation learning in ASR: reality or illusion? INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association , Sep 2019, Graz, Austria.

[Srivastava et al., 2020] B. Srivastava, N. Tomashenko, X. Wang, E. Vincent, J. Yamagishi, M. Maouche, A. Bellet, and M. Tommasi: Design choices for x-vector based speaker anonymization. INTERSPEECH 2020, 21th Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai, China.

[Turan et al., 2020] T. Turan, E. Vincent, and D. Jouvet: Achieving multi-accent ASR via unsupervised acoustic model adaptation. INTERSPEECH 2020, 21th Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai, China.

[Xiong et al., 2017] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig. Achieving human parity in conversational speech recognition. Technical report, arXiv:1610.05256, 2017.

 

Additional information:

Supervision and contact:

Denis Jouvet (denis.jouvet@inria.fr; https://members.loria.fr/DJouvet/)

Duration: 2 years

Starting date: autumn 2020

Location: Inria Nancy – Grand Est, 54600 Villers-lès-Nancy

 

 

footnote [1] : Note that when sharing data, users may want not to share data conveying private information at the linguistic level (e.g., phone number, person name, …). Such privacy aspects also need to be taken into account, but they are out-of-the scope of this project.

 

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6-23(2020-10-21) Fully funded PhD position at >IDIAP, Martigny, Switzerland

There is a fully funded PhD position open at Idiap Research Institute on 'Speech
recognition and natural language processing for digital interviews'.

At a high level, we are interested in how candidates for jobs respond in structured
selection interviews; in particular how they are able to tell stories about past work
situations. More concretely, we will investigate speech recognition architectures
suitable for such interviews, and natural language processing solutions that are able to
infer the higher level semantics required by our collaborators in the social sciences.

A-priori, given that it is the higher level semantics that are of interest, we expect to
make use of recent lexicon-free approaches to speech recognition. We also expect to draw
from the rapidly advancing language modelling field with tools such as BERT and its
successors. There will be ample opportunity for research in the component technologies,
which are all currently pertinent in the general machine learning landscape. In
particular, we expect to make advances in the technological interfaces between component
technologies, and in the humanist interfaces between the machine learning practitioners
and social scientists.

For more information, and to apply, please follow this link:
 http://www.idiap.ch/education-and-jobs/job-10312

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

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6-24(2020-10-23) PhD grant at Université Grenoble-Alpes, France.

Dans le cadre de la Chaire ' Bayesian Cognition and Machine Learning for Speech
Communication' (http://www.gipsa-lab.fr/projet/MIAI-Speech/) financée par Le
Multidisciplinary Institute in Artificial Intelligence (MIAI) de l'Université Grenoble
Alpes (https://miai.univ-grenoble-alpes.fr/), nous proposons une thèse sur le contrôle de
la production de la parole. Cette thèse étudiera comment planification et exécution
motrices interagissent pour atteindre, dans des conditions très variées et parfois
variables au cours de l'élocution, les buts auditifs et somatosensoriels qui permettent
une communication parlée efficace. Pour cela le travail consistera à implémenter et à
approfondir l'hypothèse selon laquelle le cerveau développe et exploite des
représentations ou modèles internes de la dynamique des articulateurs et de leur
influence sur l'acoustique, afin de prédire à chaque instant l'état du système
articulatoire et les corrélats auditifs du signal acoustique. Une attention particulière
sera portée sur la modélisation de la façon dont le cerveau peut intégrer en temps réel
ces prédictions avec les retours somatosensoriels et auditifs effectifs, bruités et
retardés par les temps de transmission dans les systèmes physiques et physiologiques,
pour assurer une prononciation correcte des sons, en toute condition et, en visant une
minimisation de l'effort produit.

Mots Clés :Contrôle moteur de la parole ; Contrôle moteur ; Théorie du contrôle ;
Sciences Cognitives ; Machine Learning

Pour plus d'information voir:
http://www.gipsa-lab.fr/projet/MIAI-Speech/thesis/DEEPTONGUE_PhD_Proposal_Baraduc_Perrier.pdf
Bien cordialement

Pierre Baraduc et Pascal Perrier

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6-25(2020-10-26) Post doctoral position at the Beckman Institute for Advance Science and Technology, University of Illinois, Urbana-Chanpaign, USA

Postdoctoralposition in Mobile Sensing and Child Mental Health

Beckman Institute for Advanced Science & Technology

University ofIllinois at Urbana-Champaign

 

Our interdisciplinary research team at the Beckman Institute for Advance Science and Technology is developing and applying innovative tools and methods from mobile sensing, signal processing and machine learning to gain insight into the dynamic processes underlying the emergence of disturbances in child mental health. We have engineered a wearable sensing platform that captures speech, motion, and physiological signals of infants and young children in their natural environments, and we are applying data-driven machine-learning approaches and dynamic statistical modeling techniques to large-scale, naturalistic, and longitudinal data sets to characterize dynamic child-parent transactions and children’s developing stress regulatory capacities and to ultimately capture reliable biomarkers of child mental health disturbance.

 

We seek outstanding candidates for a postdoctoral scholar position that combines multimodal sensing and signal processing, dynamic systems modeling, and child mental health. The ideal candidate would have expertise in one or more of the following domains related to wearable sensors:

 

  • signal processing of audio, motion or physiological data

  • statistical modeling of multivariate time series data

  • mobile health interventions including wearable sensors

  • activity recognition

  • machine learning

  • digital phenotyping

 

In addition to joining a highly interdisciplinary team and making contributions to high impact research on mobile sensing and child mental health, this position provides strong opportunities for professional development and mentorship by faculty team leaders, including Drs. Mark Hasegawa-Johnson, Romit Roy Choudhury, and Nancy McElwain. In collaboration with the larger team, the postdoctoral scholar will play a central role in preparing conference papers and manuscripts for publication, contributing to the preparation of future grant proposals, and assisting with further development of our mobile sensing platform for use with infants and young children.

 

Applicants should have a doctoral degree in computer engineering, computer science, or a field related to data analytics of wearable sensors, as well as excellent skills in programming, communication, and writing. Appointment is for at least two years, contingent on first-year performance. The position start date is negotiable.

 

Please send a coverletter and CV to Drs. Mark Hasegawa-Johnson (jhasegaw@illinois.edu) and Nancy McElwain (mcelwn@illinois.edu). Applications will be considered until the position is filled, with priority given to applications submitted by November 15th.

 

The University of Illinois is an Equal Opportunity, Affirmative Action employer. Minorities, women, veterans and individuals with disabilities are encouraged to apply. For more information, visit http://go.illinois.edu/EEO.

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6-26(2020-10-28) TWO positions in Trinity College Dublin, Ireland

we have openings for TWO positions in Trinity College Dublin, Ireland, available from Dec 1st, 2020 for 14 months. We are seeking:

 

A Research Assistant (qualified to Masters level)

A Research Fellow (holds a PhD)

 

The Project:

RoomReader is a project led by Prof. Naomi Harte in TCD and Prof. Ben Cowan in UCD, Ireland. The research is exploring and modelling online interactions, and is funded by the Science Foundation Ireland Covid 19 Rapid Response Call. The candidate will be working with a team to drive research into multimodal cues of engagement in online teaching scenarios. The work involves a collaboration with Microsoft Research Cambridge, and Microsoft Ireland.

The Research Assistant will have a psychology/linguistics/engineering background (we are flexible) and will be tasked with researching and designing a new online task to elicit speech based interactions relevant to online teaching scenarios (think multi-party MapTask or Diapix, but different). They will also be responsible for the capture of that dataset and subsequent editing/labelling for deployment in the project and eventual sharing with the wider research community. Annual gross salary up to ?34,930 per annum depending on experience.

The Research Fellow needs a background, including a PhD, in deep learning and the modelling of multimodal cues in speech. Their previous experience might be in conversational analysis, multimodal speech recognition or other areas. They should have a proved track record with publications commensurate with career stage. Annual gross salary up  to ?50030 depending on experience.

 

The project starts on Dec 1st, and the positions can start from that date and continue for 14 months. Please email nharte@tcd.ie for a more detailed description of either role, or to discuss. I am open to a person remote-working for the remainder of 2020, but the ideal candidate will be in a position to move to Ireland for Jan 2021 and work with the team in TCD.

 

Sigmedia Research Group @ Trinity College Dublin, Ireland

The Signal Processing and Media Applications (aka Sigmedia) Group was founded in 1998 in Trinity College Dublin, Ireland. Originally with a focus on video and image processing, the group today spans research in areas across all aspects of media ? video, images, speech and audio. Prof. Naomi Harte leads the Sigmedia research endeavours in human speech communication. The group has active research in audio-visual speech recognition, evaluation of speech synthesis, multimodal cues in human conversation, and birdsong analysis. The group is interested in all aspect of human interaction, centred on speech. Much of our work is underpinned by signal processing and machine learning, but we also have researchers with a background in linguistic and psychology aspects of speech processing to keep us all grounded.

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6-27(2020-10-30) 3 Tenure Track Professors (W2) at Saarland University, Germany

3 Tenure Track Professors (W2) at Saarland University

Saarland University is seeking to hire up to

3 Tenure Track Professors (W2)

in computer science and related areas with six-year tenure track to a permanent
professorship (W3). We are looking for highly motivated young researchers in any modern
area of Computer Science, especially in one or more of the following research areas:

. Artificial Intelligence, Machine Learning
. Natural Language Processing
. Data Science, Big Data
. Graphics, Visualization, Computer Vision
. Human-Computer Interaction
. Programming Languages and Software Engineering
. Computer Architecture and High-Performance Computing
. Networked, Distributed, Embedded, Real-Time Systems
. Bioinformatics
. Computational Logic and Verification
. Theory and Algorithms
. Societal Aspects of Computing
. Robotics
. Quantum Computing

The position will be established in the Department for Computer Science or in the
Department for Language Science and Technology of Saarland University, which is part of
the Saarland Informatics Campus (SIC), located in Saarbrücken. With its 800 researchers
and more than 2.000 students from 81 countries, the SIC belongs to one of the leading
locations for Computer Science in Germany and in Europe. All areas of computer science
are covered at five globally renowned research institutes and three collaborating
university departments, as well as in a total of 21 academic programs.

Our Offer: Tenure track professors (W2) have faculty status at Saarland University,
including the right to supervise bachelor?s, master?s and PhD students. They focus on
world-class research, will lead their own research group and will have a significantly
reduced teaching load. In case of outstanding performance, the position will be tenured
as full professor (W3). The tenure decision is made after at most 6 years.

The position provides excellent working conditions in a lively scientific community,
embedded in the vibrant research environment of the Saarland Informatics Campus. Saarland
University has leading departments in Computer Science and Computational Linguistics,
with more than 350 PhD students working on timely topics (see
https://saarland-informatics-campus.de/ for additional information).

Qualifications:

Applicants must hold an outstanding PhD degree, typically have completed a postdoc stay
and have teaching experience. They must have demonstrated outstanding research abilities
and the potential to successfully lead their own research group. Active contribution to
research and teaching, including basic lectures in computer science, is expected.
Teaching languages are English or German (basic courses). We expect sufficient German
language skills after an appropriate period.

Your Application: Candidates should submit their application online at:
https://applications.saarland-informatics-campus.de
No additional paper copy is required. The application must contain:

. a cover letter and curriculum vitae
. a full list of publications
. a short prospective research plan (2-5 pages)
. copies of degree certificates
. full text copies of the five most important publications
. a list of references: 3-5 (including email addresses), at least one of whom must be
  a person who is outside the group of your current or former supervisors or
  colleagues.

Applications will be accepted until December 11th, 2020. Application talks will take
place between Feb. 01 and Feb. 26, 2021. Please refer to reference W1786 in your
application. Please contact apply@saarland-informatics-campus.de if you have any
questions.

Saarland University is an equal opportunity employer. In accordance with its policy of
increasing the proportion of women in this type of employment, the University actively
encourages applications from women. For candidates with equal qualifications, preference
will be given to people with disabilities. In addition, Saarland University regards
internationalization as a university-wide cross-sectional task and therefore encourages
applications that align with activities to further internationalize the university. When
you submit a job application to Saarland University you will be transmitting personal
data. Please refer to our privacy notice (https://www.uni-
saarland.de/verwaltung/datenschutz/) for information on how we collect and process
personal data in accordance with Art. 13 of the General Data Protection Regulation
(GDPR). By submitting your application you confirm that you have taken note of the
information in the Saarland University privacy notice.

Links:

https://www.uni-saarland.de/fileadmin/user_upload/verwaltung/stellen/wissenschaftler/W1786_W2TT-Informatik_Ausschreibung_final.pdf

Saarland University, https://www.uni-saarland.de/en/home.html

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6-28(2020-10-30) 2 Research Assistant/Associate Posts at Cambridge University, Great Britain
2 Research Assistant/Associate Posts in Spoken Language Processing at Cambridge University (Fixed Term)
 
Applications are invited for two Research Assistants/Research Associates in the Department of Engineering, Cambridge University, to work on an EPSRC funded project Multimodal Video Search by Examples (MVSE). The project is a collaboration between three Universities, Ulster, Surrey and Cambridge, and the BBC as an industrial partner. The overall aim of the project is to enable effective and efficient multimodal video search of large archives, such as BBC TV programmes.

The research associated with these positions will focus on deriving representation for all the information that is contained within the video speech signal, and integrating with other modalities. The forms of representation will include both: voice analytics e.g. speaker and emotion; and topic and audio content analytics e.g. word-sequence and topic classification and tracking. The position will involve close collaboration with Surrey and Ulster Universities to integrate with other information sources, video and the audio scene, to yield a flexible and efficient video search index.

Fixed-term: The funds for this post are available until 31 January 2024 in the first instance
Closing date: 1st December 2020

Full information can be found at: http://www.jobs.cam.ac.uk/job/27458/
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6-29(2020-11-02) Fully-funded PhD studentships at the University of Sheffield, Great Britain

 Fully-funded PhD studentships in Speech and NLP at the University of Sheffield

*******************************************************************************************************

 

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, UK

 

Fully-funded 4-year PhD studentships for research in speech technologies and NLP

 

** Applications now open for September 2021 intake **

 

Deadline for applications: 31 January 2021. 

 

Speech and Language Technologies (SLTs) are a range of Artificial Intelligence (AI) approaches which allow computer programs or electronic devices to analyse, produce, modify or respond to human texts and speech. SLTs are underpinned by a number of fundamental research fields including natural language processing (NLP / NLProc), speech processing, computational linguistics, mathematics, machine learning, physics, psychology, computer science, and acoustics. SLTs are now established as core scientific/engineering disciplines within AI and have grown into a world-wide multi-billion dollar industry.

 

Located in the Department of Computer Science at the University of Sheffield ? a world leading research institution in the SLT field ? the UKRI Centre for Doctoral Training (CDT) in Speech and Language Technologies and their Applications is a vibrant research centre that also provides training in engineering skills, leadership, ethics, innovation, entrepreneurship, and responsibility to society.

 

Apply now: https://slt-cdt.ac.uk/apply/ 

 

The benefits:

  • Four-year fully-funded studentship covering 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 Postgraduate Diploma (PGDip) incorporating 6 months of foundational speech and NLP training prior to starting your research project 

  • Bespoke cohort-based training programme running over the entire four years providing the necessary skills for academic and industrial leadership in the field.

  • 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 is underpinned by a real-world application, directly supported by one of our industry partners. 

  • 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 UK university cities (WhatUni? 2019).

 

About you:

We are looking for students from a wide range of backgrounds interested in speech and NLP. 

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

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

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

  • The majority of candidates must satisfy the UKRI funding eligibility criteria for ?home? students. Full details can be found on our website.

 

Applying:

Applications are now sought for the September 2021 intake. The deadline is 31 January 2021. 

 

Applications will be reviewed within 6 weeks of the deadline and short-listed applicants will be invited to interview. Interviews will be held in Sheffield or via videoconference. 

 

See our website for full details and guidance on how to apply: https://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. 

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