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ISCApad #294 |
Saturday, December 10, 2022 by Chris Wellekens |
6-1 | (2022-07-07) Two internship positions @ Naver Labs Europe Naver Labs Europe (https://europe.naverlabs.com/) is currently offering 2 internship positions related to Speech Processing.
More details on both job offers can be found here:
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6-2 | (2022-07-17) Two PhD positions at Quality and Usability Lab, Technical University of Berlin, Germany Two PhD positions at Quality and Usability Lab, Technical University of Berlin, Germany
Two PhD positions at Quality and Usability Lab, Technical University of Berlin, Germany
We are looking to recruit two Doctoral Researchers to join Quality and Usability Lab, at Technical University of Berlin, Germany. Both positions are research assistant positions (TVL-E13) and depending on follow up funding to be continued until doctorate thesis can be finished. The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.
The research is in the area of the assessment of the quality of speech services using a crowdsourcing approach. The aim of the research is to analyze how crowdsourcing-based listening-only and conversational speech quality evaluation experiments can be set up in order to provide valid and reliable results, and how the characteristics of the test participants, the test environment and the playback system can be assessed in online tests. It will be assessed which differences are to be expected between crowdsourcing and laboratory-based speech quality evaluation, and how these differences influence the development of instrumental speech quality prediction models. The results are expected to influence methods for speech quality assessment in crowdsourcing, as they are summarized in ITU-T Recommendation P.808. This project is funded by the Deutsche Forschungsgemeinschaft, DFG, and is limited to a duration until January 31, 2024 (compensation TVL E13). A subsequent ongoing employment is supported if the PhD cannot be finished in running time of the project
The position is open to do research in the field of speech signals analysis, and the assessment of speech quality in different (mobile and fixed) networks. Therefore, speech signals are to be analyzed in listening-only as well as conversational situations in order to get indications or the perceived quality. Based on these analysis, signal-based and parametric models for the estimation of speech quality can be extended and integrated. One focus of the present research may be the evaluation of new speech codecs in different network scenarios. The models are to be validated based on subjective listening and conversation tests.
The initial funding is available from September 1st, 2022 and is limited until April 30th, 2023; however, the outcomes of the research should be used to support the preparation of a new project application, and may also become a foundation for a later PhD thesis. A subsequent position as a research assistant from the project funds would be possible if the funds were approved.
2.1 Tasks
2.2. Requirements
Application For both positions, please send the following documents, bundled in a single PDF file, to Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de: Letter of application, curriculum vitae, copies of certificates, job references. Please also specify for which position you are applying. To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored.
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6-3 | (2022-07-28) PhD Position : Naver Labs Europe (France) and FBK Trento (Italy)PhD Position : Naver Labs Europe (France) and FBK Trento (Italy) start Nov 2022Have you recently completed or expect very soon an MSc or equivalent degree in computer science, artificial intelligence, computational linguistics, engineering, or a related area? Are you interested in carrying out research on Speech-to-Speech Translation during the next few years? Are you excited to spend a part of your life in 2 pleasant alpine cities in France (Grenoble) and Italy (Trento) ?
WE ARE LOOKING FOR YOU!!!
The Machine Translation (MT) group at Fondazione Bruno Kessler (Trento, Italy) in conjunction with Naver Labs Europe (Grenoble, France) are pleased to announce the availability of the following fully-funded Ph.D. position at the Doctorate Program in Industrial Innovation of the University of Trento and Fondazione Bruno Kessler.
PhD topic: Unified Foundation models for Speech-to-Speech Translation
The deadline for application: August 23rd.
More details here: http://tinyurl.com/PhD-FBK-NLE
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6-4 | (2022-07-21) Research Opportunity at INESC TEC / LIAAD, Porto, Portugal Research Opportunity at INESC TEC / LIAAD, Porto, Portugal Funded PhD position, fees covered during the period of the grant
Application deadline 02-August-2022
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6-5 | (2022-08-11) PhD position @Gipsa Lab , Grenoble, France L'offre de thèse sur 'le Rôle de la conduction osseuse dans le contrôle de la voix (parole, chant) et de l'expression musicale' (Role of bone conducted feedback in the control of voice (speech, singing) and musical expression) n'a toujours pas été pourvue : Si vous souhaitez candidater, aller sur le lien suivant :
Bien cordialement, Pierre Baraduc, Coriandre Vilain et Nathalie Henrich Bernardoni *********************************************************************************************************** Offre de thèse sur 'le Rôle de la conduction osseuse dans le contrôle de la voix (parole, chant) et de l'expression musicale' (Role of bone conducted feedback in the control of voice Pour candidater, aller sur le lien suivant : https://emploi.cnrs.fr/Offres/Doctorant/UMR5216-CHRROM-018/Default.aspx
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6-6 | (2022-08-22) PhD in ML/NLP – Fairness and self-supervised learning for speech processing, IMAG, Grenoble,France
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6-7 | (2022-08-22) PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processi
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6-8 | (2022-08-24) PostDoc position at Grenoble Alps University, Grenoble, France PostDoc position at Grenoble Alps University, France
Summary
The Grenoble Alps University offers a PostDoc position for a highly motivated candidate to be working on the multi-disciplinary research project THERADIA, which aims to create an empathic virtual assistant that accompanies cognitively impaired patients during remediation exercises at home. The successful candidate will have the exciting opportunity to develop new machine learning techniques for the robust detection of affective and cognitive behaviours from newly collected audiovisual data. Models will be incorporated into the virtual agent to tailor the interaction with the patient, using specific interaction scenarios, and these models will be evaluated and fine-tuned in a clinical trial to demonstrate the effectiveness of the agent in supporting patients suffering from cognitive conditions during digital therapies. If successful, the system will be operated nationally and the cognitive remediation sessions will be covered by social security. Duration: 2 years,
Salary: according to experience (up to 4142€ / month)
Envisaged starting date: November 2022
Scientific environment
The person recruited will be hosted within the GETALP team of the Laboratoire d’Informatique de Grenoble (LIG), which offers a dynamic, international, and stimulating framework for conducting high-level multi-disciplinary research. The GETALP team is housed in a modern building (IMAG) located on a 175-hectare landscaped campus that was ranked as the eighth most beautiful campus in Europe by Times Higher Education magazine in 2018.
Requirements
The ideal candidate must have a PhD degree and a strong background in machine learning, and affective computing or cognitive science/neuroscience.
The successful candidate should have:
· Excellent knowledge of machine learning techniques
· Good knowledge of speech and/or image processing
· Good knowledge of experimental design and statistics
· Strong programming skills in Python
· Excellent publication record
· Willing to work in multi-disciplinary and international teams
· Good communication skills
Application
Applications are expected to be received on an ongoing basis and the position will be open until filled. Applications should be sent to Fabien Ringeval (fabien.ringeval@imag.fr) and François Portet (francois.portet@imag.fr). The application file should contain:
· Curriculum vitae
· Recommendation letter
· One-page summary of research background and interests
· At least three publications demonstrating expertise in the aforementioned areas
· Pre-defence reports and defence minutes; or summary of the thesis with date of defence for those currently in doctoral studies
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6-9 | (2022-08-25) Post-doc @IMT Atlantique, France In the framework of the European/Japanese e-VITA project (https://www.e-vita.coach/), IMT Atlantique is offering a 15-month post-doctoral position in the field of active living technologies (IoT, data fusion, AI, cloud/edge architectures, user services, coaching, NLP, etc.).
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6-10 | (2022-08-26) Ingénieur.e de recherche, Laboratoire Parole et Langage, Aix-en-Provence, France Plateforme EEG & Oculométrie qui aura pour missions :
Il s'agit d'un CDD de 4 mois à partir de 10/2022 à Aix-en-Provence.
Info & candidature : https://emploi.cnrs.fr/Offres/CDD/UMR7309-STELHU-003/Default.aspx
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6-11 | (2022-09-02) Research Assistant, TUBerlin, GermanyResearch Assistant for Conversational Speech Quality Assessment and Prediction - salary grade E 13 TV-L Berliner Hochschulenpart-time employment may be possible Hire DateThe start date for the position is planned for November 1st, 2022, qualification goal doctorate. It is limited to a duration until September 30th, 2025 and a subsequent ongoing employment is supported if sufficient third-party-funding is available. About UsThe majority of systems and services that are provided by computer science, electrical engineering and information technology finally are directed to their human users. To successfully build such systems and services, it is essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception and communication, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.TasksThe position is open to do research in the fieldof speech signals analysis, and the assessment of speech quality in different (mobile and fixed) networks. Therefore, speech signalsare to be analyzed in listening-only as well as conversational situations in order to get indications or the perceived quality. Based on these analysis, signal-based and parametric models for the estimation of speech quality can be extended and integrated. One focus of the present research may be the evaluation of new speech codecs in different network scenarios. The models are to be validated based on subjective listening and conversation tests. For this purpose, methods of crowdsourcing can be applied, i. e. real users should carry out the data collection and/or evaluation via an online platform. Scientifically interesting is the comparison of such crowdsourced data to those that can be obtained under laboratory conditions.
The outcomes of the research should be used to support the preparation of a new project application, and may also become a foundation for a later PhD thesis. A subsequent position as a research assistant from the project funds would be possible if the funds were approved.
The concrete tasks include, among other things: Maintaining and further developing a platform to conduct web-based voice calls
Requirements
Compensation
“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%). ApplicationPlease send the following documents, bundled in a single PDF file, to Letter of application, curriculum vitae, copies of certificates, job references.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.
Please send electronic copies only. Original documents will not be returned.
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6-12 | (2022-09-02) Research Assistant (2), TUBerlin, GermanyResearch Assistant for Chatbot-based Support for Self-organization During Studies - salary grade E 13 TV-L Berliner Hochschulenpart-time employment may be possible Hire DateThe start date for the position is planned for October 1st, 2022, with the qualification goal doctorate. It is limited to a duration of 26 months and a subsequent ongoing employment is supported if the PhD cannot be finished in the given time. About UsMost systems and services that are provided by computer science, electrical engineering and information technology finally are oriented on the needs of their human users. To build successfully build such systems and services it essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.TasksConception and development of text-based interactive dialogue systems, so-called chatbots, as part of the USOS project (chatbot-based support for self-organization during studies). Machine learning methods are used both to process text-based information and to control dialogs. The range of tasks also includes the design and implementation of graphic user interfaces, e.g., as a web app, Android app or iOS app. The quality and the user experience of the created interaction concepts are then evaluated in the context of user studies.
The specific tasks include:
Professionally experienced employees from our team support you with self-motivated familiarization with the areas of responsibility.Requirements
Compensation“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%) ApplicationPlease send the following documents, bundled in a single PDF file, to Letter of application, curriculum vitae, copies of certificates, job references.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored.
Please send copies only. Original documents will not be returned.
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6-13 | (2022-09-02) Research assistant (3), TUBerlin, GermanyResearch Assistant for Multimodal Interactive Assistance for the Digital Collection of Patient-Reported Outcome Measures - salary grade E 13 TV-L Berliner Hochschulenpart-time employment may be possible Hire DateThe start date for the position is planned for December 1st, 2022, with the qualification goal doctorate. It is limited to a duration of 2.5 years and a subsequent ongoing employment is supported if the PhD cannot be finished in the given time. About UsMost systems and services that are provided by computer science, electrical engineering and information technology finally are oriented on the needs of their human users. To build successfully build such systems and services it essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.The Quality and Usability Lab (https://tu.berlin/qu) is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.TasksConception and development of an interactive natural language-based dialog system, as part of the project MIA-PROM (Multimodal interactive assistance for the digital collection of Patient-Reported Outcome Measures). The project is in the field of outpatient rehabilitation and requires cooperation with researchers (human-machine interaction, technology-sociology, and healthcare) and the intended users. In the subproject Adaptive Dialog, methods of machine learning are used both for the processing of natural language utterances and for the control and adaptation of dialogs. The range of tasks also includes research on the methods and interaction concepts used in the subproject. The quality and user experience of the created interaction concepts are then evaluated in empirical user studies.
The specific tasks include:
Professionally experienced employees from our team support you with self-motivated familiarization with the areas of responsibility.Requirements
Compensation“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%) ApplicationPlease send the following documents, bundled in a single PDF file, to Letter of application, curriculum vitae, copies of certificates, job references.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored.
Please send copies only. Original documents will not be returned.
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6-14 | (2022-09-09) Postdoctoral Research Fellow, Tampere University and Tampere University of Applied Sciences, Finland Postdoctoral Research Fellow
(speech and language technology,cognitive science) Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high- impact research and education. Our universities community has its competitive edges in technology, health and society.www.tuni.fi/en Speech and Cognition research group (SPECOG )is part ofComputing Sciences atTampere University within the Faculty of Information Technology and Communication Sciences.SPECOG focuses on interdisciplinary research at the intersection of speechand languagetechnology and cognitive science.We combine advanced signal processing and machine learning methods with empirical large-scale infant data to the study of child language development. We also study how human-like perceptual learning can be applied in artificial intelligence (AI) systems. SPECOG collaborates with several internationally leading research groups within and across disciplinary boundaries, including joint research with psychologists, linguists, phoneticians, and computer scientists. More informationon SPECOG:https://webpages.tuni.fi/specog/index.html Job description We are inviting applications for the position o fa postdoctoral research fellow on the topic of computational modelling of child language development. The position is associated with aproject titled “Modeling Child Language Development using Naturalistic Data at a Scale (L-SCALE)”, where the aim is to develop new practices to training and evaluation of computational models of infant language learning using realistic infant data. These data may include long-form child-centred audio recordings,infant-care giver interaction transcripts, and meta-analyses conducted across a range of behavioural experiments.While the job is located in Finland, the project has a notable emphasis on international collaboration with key partners around the world. The work will be conducted as a member of the SPECOG research groupled by Dr.Okko Räsänen.We are looking for candidates who are interested in human and/or machine language processing, and who are willing to contribute to our cross-disciplinary research efforts in understanding language learning in humans through computational means. In this position, the candidate is expected to: 1)carry out high-quality postdoctora lresearch on computational modelling of early language development and contribute to the development of ecologically plausible model training and evaluation practice. 2)work in close collaboration with other members of the research group, and 3)advise undergraduate/graduate projects on topics related to your own research (with flexibility according to personal interests and career aspirations). Requirements The candidate should hold a doctoral degree (e.g.,PhD or D.Sc.) in language technology, psycholinguistics, cognitive science,computer science,or other relevant area. Candidates who have already completed their doctoral research work but have not yet received their doctoral certificate may also apply. A successful candidate has strong expertise in a)speech and/or language technology or in b) childlanguage Fluent programming (at least Python,Matlab, or R) and oral and written English skills are About the research environment
Finland is among the most stable, free and safe countries in the world, based on prominent ratings by various agencies. It is also ranked as one of the top countries as far as social progress is concerned. Tampere is the largest inland city of Finland, and the city is counted among the major academic hubs in the Nordic countries, offering a dynamic living environment. Tampere region is one of the most rapidly growing urban areas in Finland and home to a vibrant knowledge-intensive entrepreneurial community. The city is an industrial powerhouse that enjoys a rich cultural scene and a reputation as a centre of Finland’s information society. Despite its growth, living in Tampere is highly affordable for housing. In addition,the excellent public transport network enables quick, easy,and cheap transportation around the city of Tampere and university campuses.Tampere is also surrounded by vivid nature with forests and lakes, providing countless opportunities for easy-to-access outdoor adventures and refreshment throughout the year. Read more about Finland and Tampere: •https://www.visitfinland.com/about-finland/ •https://finland.fi/ •http://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/161193/MEAEguide_18_2018_T ervetuloaSuomeen_Eng_PDFUA.pdf •https://visittampere.fi/en/
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6-15 | (2022-09-12) Postdocs and Software engineering positions, Telecom Paris, France
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6-16 | (2022-09-27) Deep learning software expert, CNRS:LSCP, Paris, France Short summary: We are looking for someone with experience with deep learning, ideally using scikit-learn & pytorch, to join our technical team. We specialize in long-form audio-recordings, and your job will be to design, fine-tune, and evaluate neural networks on such data. Conversational French is NOT needed - we work in English!
For more details see https://emploi.cnrs.fr/Offres/CDD/UMR8554-ALECRI1-001/Default.aspx?lang=EN
--------------------------------------------------------------- Alex (Alejandrina) Cristia Researcher, CNRS Laboratoire de Sciences Cognitives et Psycholinguistique 29, rue d'Ulm, 75005, Paris, FRANCE My site: www.acristia.org
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6-17 | (2022-09-25) Post-doctorat (H/F) Identification d'expressions genrées at LISN, St Aubin, France Le LISN recrute un post-doc d’un an dans le cadre du projet ANR GEM (Gender Equality Monitor) sur l’identification d'expressions genrées par des représentations vectorielles sur un corpus de transcription de la parole dans les médias. l’offre est détaillée ci-dessous : Post-doctorat (H/F) Identification d'expressions genrées par des représentations vectorielles sur un corpus de transcription de la parole dans les médias Informations générales Référence : UMR9015-CYRGRO-002 Lieu de travail : ST AUBIN Date de publication : samedi 10 septembre 2022 Type de contrat : CDD Scientifique Durée du contrat : 12 mois Date d'embauche prévue : 1 décembre 2022 Quotité de travail : Temps complet Rémunération : Entre 2889,91€ et 4082,9€ bruts mensuels selon expérience Niveau d'études souhaité : Doctorat Expérience souhaitée : 1 à 4 années Missions Le projet GEM (Gender Equality Monitor) vise à analyser les interactions entre femmes et hommes dans les médias (radio et télévision), et plus particulièrement les différences de représentations selon que la personne qui s'exprime est une femme ou un homme, selon son rôle (anonyme, journaliste, politique, etc.), et selon les thèmes abordés. Dans ce projet inter-disciplinaire, les partenaires informatiques (dont le LISN) ont pour mission d'implémenter les descripteurs qui permettront aux partenaires en sciences humaines et sociales de quantifier et qualifier les différences de représentation. https://anr.fr/Projet-ANR-19-CE38-0012 Activités La personne recrutée (H/F) aura en charge de mettre au point des techniques de traitement automatique des langues (TAL) non supervisées ou semi-supervisées appliquées à des corpus de transcriptions automatiques de la parole, pour identifier les 'expressions genrées' telles que les références à des stéréotypes culturels en fonction du genre, les entités nommées traditionnelles ou toute référence à la vie privée, l'âge, le physique, la sexualité, les compétences, etc. De manière secondaire, l'analyse des biais dans les modèles de langue pourra également être conduite. Les corpus sont mis à disposition par le porteur du projet (Institut National de l'Audiovisuel) et se composent : de matinales radios et journaux de télévision du corpus GMMP (Global Monitoring Media Project), d'émissions de radio françaises (émissions culinaires, économiques, sportives, et libre-antennes) pour l'étude des incivilités (interruptions, injures, etc.), et d'émissions de télé-réalité (Loft Story 2001, Les Marseillais à Dubaï 2021). Aucune annotation n'est disponible autour des expressions genrées. La personne recrutée devra donc privilégier des méthodes non supervisées ou semi-supervisées. Ce travail sera co-encadré par Mme Sahar Ghannay (MCF en informatique à l'Université Paris Saclay) et M. Cyril Grouin (IR en informatique au CNRS). Le contrat sera financé par l'Agence Nationale de la Recherche (ANR GEM 2019) porté par David Doukhan (Institut National de l'Audiovisuel). Compétences - très bonne maîtrise du français - traitement automatique des langues et de la parole ; une formation spécifique dans cette discipline est un plus - expérience des plongements lexicaux et réseaux de neurones Contexte de travail Le Laboratoire Interdisciplinaire des Sciences du Numériques (LISN) est une unité installée sur le plateau de Saclay et créée en 2021 de la fusion des laboratoires LIMSI et LRI. Les recherches effectuées au LISN couvrent un large spectre scientifique et sont reconnues à l'international. Le laboratoire comprend plus de 380 membres répartis dans 16 équipes de recherche et 6 services de support et soutien. Les locaux sont intégralement en zone à régime restrictif (ZRR). La personne recrutée travaillera au sein de l'équipe ILES, en lien étroit avec les chercheurs des équipes ILES et TLP impliqués sur le projet, au sein du département Sciences et Technologies des Langues (STL). Contraintes et risques Déplacement possible en Ile-de-France pour les réunions de travail ponctuelle Déplacements nationaux et internationaux en conférence en cas d'article à présenter Travail sur ordinateur
Candidature ici: https://emploi.cnrs.fr/Offres/CDD/UMR9015-CYRGRO-002/Default.aspx
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6-18 | (2022-10-12) Internships at AVA France We have two 6 months internship proposals (for M2/Master 2 level) at Ava France ( https://www.ava.me/ ) in Paris (possible remote) on speech diarization. Feel free to apply:
Best regards,
Alexey Ozerov AI Research Lead at Ava
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6-19 | (2022-10-25) Postdoc@Telecom Paris (France) Post-Doctoral Position on Neural Models for Dialog Analysis Matthieu Labeau, Gaël Guibon, Chloé Clavel
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6-20 | (2022-10-14) Internships @ IRIT Toulouse, France L’équipe SAMoVA de l’IRIT à Toulouse propose plusieurs stages (M1, M2, PFE ingénieur) en 2023 autour des thématiques suivantes (liste non exhaustive) :
- traitement de la parole atypique
- modélisation de la déglutition
- transcription et compréhension de la parole (spoken language understanding)
- segmentation et regroupement en locuteurs (speaker diarization)
- description textuelle de l'audio (audio captioning)
Tous les détails (sujets, contacts) sont disponibles dans la section 'Jobs' de l’équipe :
https://www.irit.fr/SAMOVA/site/jobs/ Hervé Bredin
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6-21 | (2022-10-12) Ingenieur AI@INE France Nous recherchons activement un ingénieur en charge des opérations pour le département Évaluation de l’intelligence artificielle et de la cybersécurité du LNE :
Le candidat retenu intégrera une équipe en forte croissance spécialisée en évaluation des systèmes d’IA et intervenant dans de nombreux domaines (TAL, traitement d’images, dispositifs médicaux intelligents, systèmes de mobilité autonomes, robots agricoles, cobots, etc.).
Je me tiens à votre disposition pour tout échange sur cette offre.
Merci d’avance pour vos candidatures et vos partages, à très bientôt ! Guillaume AVRIN, PhD Direction des essais et de la certification
Laboratoire national de métrologie et d'essais
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6-22 | (2022-10-12) Positions @University of Texas El Paso, TX, USA Two 3yr postdoc positions testing gesture-speech synchrony
We're looking for two smart and motivated postdocs to join the Speech Perception in Audiovisual Communication lab (SPEAC; https://hrbosker.github.io) at the Donders Institute, Radboud University, Nijmegen, The Netherlands.
Keywords: multimodal prosody, audiovisual speech perception, gesture-speech synchrony, motion-tracking, MEG
>>> PD1
You will test both the production and perception of gesture-speech alignment in nine different languages, including free-stress, fixed-stress, and lexical tone languages. The production strand uses motion-tracking of 2D videos in Mediapipe and acoustic analyses in Praat to quantify gesture-speech alignment on a millisecond timescale. The perception strand involves running psychoacoustic tests with audiovisual stimuli manipulated to vary in the synchrony between hands and spoken prosody. Combining production and perception data will reveal how language-specific variability in gesture-speech alignment shapes the language-specific use of gestural timing in speech perception.
>>> PD2
You will use rapid invisible frequency tagging (RIFT) in MEG to pinpoint the neurobiological mechanisms underlying gesture-speech integration. Specifically, you will test how simple up-and-down beat gestures influence lexical stress perception in real time, using the 'manual McGurk effect' (Bosker & Peeters, 2021, Proc Roy Soc B). Furthermore, you will compare typical behavioural and neural signatures of gesture-speech integration to those in individuals with autism spectrum disorder (ASD) who are known to demonstrate impairments in prosody processing and audiovisual integration. Finally, you will run a large-scale correlational study testing whether the participants' own gestural timing behaviour is linked to their use of gestural timing in audiovisual speech perception.
3 year contract; employment for 0.8 FTE. Gross monthly salary: min €3,974 - max €5,439 (based on 38-hour working week; scale 11). Apply by December 1, 2022. Preferred starting date: March 1, 2023.
Contact: Hans Rutger Bosker, HansRutger.Bosker@donders.ru.nl
This mail was sent through the SProSIG mailing list, which is for announcements of interest to the speech prosody research community. To subscribe/unsubscribe, send mail to list@sprosig.org.
Nigel Ward, Professor of Computer Science, University of Texas at El Paso CCSB 3.0408, +1-915-747-6827 nigel@utep.edu https://www.cs.utep.edu/nigel/
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6-23 | (2022-10-17) Research internships @ LIUM, Le Mans France Nous proposons deux stages de recherche (pour le niveau M2/Master 2) au LIUM - Le Mans Université sur le traitement de la parole.
Tous les détails sont disponibles dans la section 'Recrutements' du site du laboratoire, onglet 'Stages' :
Merci de transférer si vous connaissez des étudiant.e.s à la quête de telle opportunité.
Meilleures salutations,
Meysam Shamsi
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6-24 | (2022-10-20) Postdoc in Educational Data Mining/Learning Analytics, University of Colorado Boulder, CO, USAPostdoc in Educational Data Mining/Learning AnalyticsLocation: University of Colorado Boulder, Boulder CO, USA
Work type: Full time
Employment type: Research faculty
Anticipated Start Date: Spring 2023 (desired), Summer 2023, or Fall 2023
Salary: $70k-$100k (depending on experience and qualifications)
Position Duration: 1-3 years, Initial contract is for one year. Second year contract is based on performance and extension to a third year and beyond is possible
Brief Job Summary: In this position, you will develop and apply computational techniques to analyze data from students’ log files in conjunction with other multimodal signals (e.g., speech, facial expressions, learning artifacts) during small group human tutoring, intelligent tutoring, and collaborative problem solving. You will also assist with integrating computational models into educational technologies where their performance and impact can be assessed in the real-world.
Please visit the job details page below for more information and to apply:
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6-25 | (2022-10-25) OPEN POSITIONS @ ELDA, Paris (France) OPEN POSITIONS in Paris (France)
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6-26 | (2022-11-08) Post doc and engineering positions @ LORIA-INRIA, Nancy, France
Automatic speech recognition for non-natives speakers in a noisy environment
Post-doctoral and engineer positions
Starting date: begin of 2023
Duration: 24 months for a post-doc position and 12 months for an engineer position
Supervisors: Irina Illina, Associate Professor, HDR Lorraine University LORIA-INRIA Multispeech Team, illina@loria.fr
Context
When a person has their hands busy performing a task like driving a car or piloting an airplane, voice is a fast and efficient way to achieve interaction. In aeronautical communications, the English language is most often compulsory. Unfortunately, a large part of the pilots are not native English and speak with an accent dependent on their native language and are therefore influenced by the pronunciation mechanisms of this language. Inside an aircraft cockpit, non-native voice of the pilots and the surrounding noises are the most difficult challenges to overcome in order to have efficient automatic speech recognition (ASR). The problems of non-native speech are numerous: incorrect or approximate pronunciations, errors of agreement in gender and number, use of non-existent words, missing articles, grammatically incorrect sentences, etc. The acoustic environment adds a disturbing component to the speech signal. Much of the success of speech recognition relies on the ability to take into account different accents and ambient noises into the models used by ARP.
Automatic speech recognition has made great progress thanks to the spectacular development of deep learning. In recent years, end-to-end automatic speech recognition, which directly optimizes the probability of the output character sequence based on the input acoustic characteristics, has made great progress [Chan et al., 2016; Baevski et al., 2020; Gulati, et al., 2020].
Objectives
The recruited person will have to develop methodologies and tools to obtain high-performance non-native automatic speech recognition in the aeronautical context and more specifically in a (noisy) aircraft cockpit.
This project will be based on an end-to-end automatic speech recognition system [Shi et al., 2021] using wav2vec 2.0 [Baevski et al., 2020]. This model is one of the most efficient of the current state of the art. This wav2vec 2.0 model enables self-supervised learning of representations from raw audio data (without transcription).
How to apply: Interested candidates are encouraged to contact Irina Illina (illina@loria.fr) with the required documents (CV, transcripts, motivation letter, and recommendation letters).
Requirements & skills:
- M.Sc. or Ph.D. degree in speech/audio processing, computer vision, machine learning, or in a related field,
- ability to work independently as well as in a team,
- solid programming skills (Python, PyTorch), and deep learning knowledge,
- good level of written and spoken English.
References
[Baevski et al., 2020] A. Baevski, H. Zhou, A. Mohamed, and M. Auli. Wav2vec 2.0: A framework for self-supervised learning of speech representations, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020.
[Chan et al., 2016] W. Chan, N. Jaitly, Q. Le and O. Vinyals. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4960-4964, 2016.
[Chorowski et al., 2017] J. Chorowski, N. Jaitly. Towards better decoding and language model integration in sequence to sequence models. Interspeech, 2017.
[Houlsby et al., 2019] N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, S. Gelly. Parameter-efficient transfer learning for NLP. International Conference on Machine Learning, PMLR, pp. 2790–2799, 2019.
[Gulati et al., 2020] A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu, and R. Pang. Conformer: Convolution-augmented transformer for speech recognition. Interspeech, 2020.
[Shi et al., 2021] X. Shi, F. Yu, Y. Lu, Y. Liang, Q. Feng, D. Wang, Y. Qian, and L. Xie. The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6918–6922, 2021.
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6-27 | (2022-11-12) One-year position @Institut Mines Telecom Atlantique, Nantes, France In the framework of the European/Japanese e-VITA project (https://www.e-vita.coach/),
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6-28 | (2022-11-15) Two internships @ Zaion, Paris, France Nous proposons deux offres de stage au sein de Zaion (niveau M2).
Merci de transférer si vous connaissez des étudiant.e.s qui cherchent de telles opportunités. - Apprentissage automatique pour router intelligemment les appels entrants dans les centres d’appel :
- Génération automatique de résumé de dialogue :
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6-29 | (2022-11-16) Post-doc @LaBRI, Bordeaux, France The Bordeaux Computer Science Laboratory (LaBRI) is currently looking to fill a 1 year post-doctoral position in the framework of the FVLLMONTI European project (http://www.fvllmonti.eu) Details on the position are given below. — The University of Bordeaux invites applications for a 1 year full-time postdoctoral researcher in Automatic Speech Recognition. The position is part of the FVLLMONTI project on efficient speech-to-text translation on embedded autonomous devices, funded by the European Community. To apply, please send by email a single PDF file containing a full CV (including publication list), cover letter (describing your personal qualifications, research interests and motivation for applying), evidence for software development experience (active Github/Gitlab profile or similar), two of your key publications, contact information of two referees and academic certificates (PhD, Diploma/Master, Bachelor certificates). Details on the position are given below: Job description: Post-doctoral position in Automatic Speech Recognition Duration: 12 months Starting date: tentatively 03/01/2023 Project: European FETPROACT project FVLLMONTI (started January 2021) Location: Bordeaux Computer Science Lab. (LaBRI CNRS UMR 5800), Bordeaux, France (Image and Sound team) Salary: from 2 086,45€ to 2 304,88€/month (estimated net salary after taxes, according to experience) Contact: jean-luc.rouas@labri.fr Job description: The applicant will be in charge of optimizing our state-of-the-art Automatic Speech Recognition and Machine Translation systems for English and French built using the ESPNET framework (https://espnet.github.io/espnet/) for end-to-end deep neural networks. The objective is to match the specifications and constraints of the designed systems to the requirements of other partners of the project specialized in hardware (close work with EPFL https://www.epfl.ch/labs/esl/). In particular, the applicant will carry on the work of previous post-doctorates on compression techniques (e.g. pruning, quantization, etc.) applied to Transformer and Conformer based networks to reduce the memory and energy consumption while keeping an eye on the performances (WER and BLEU scores). When a satisfactory trade-off is reached, more exploratory work is to be carried out on using emotion/attitude/affect recognition on the speech samples to supply additional information to the translation system. Context of the project: The aim of the FVLLMONTI project is to build a lightweight autonomous in-ear device allowing speech-to-speech translation. Today, pocket-talk devices integrate IoT products requiring internet connectivity which, in general, is proven to be energy inefficient. While machine translation (MT) and Natural Language Processing (NLP) performances have greatly improved, an embedded lightweight energy-efficient hardware remains elusive. Existing solutions based on artificial neural networks (NNs) are computation-intensive and energy-hungry requiring server-based implementations, which also raises data protection and privacy concerns. Today, 2D electronic architectures suffer from 'unscalable' interconnect and are thus still far from being able to compete with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption. Recent advances in materials science, device technology and synaptic architectures have the potential to fill this gap with novel disruptive technologies that go beyond conventional CMOS technology. A promising solution comes from vertical nanowire field-effect transistors (VNWFETs) to unlock the full potential of truly unconventional 3D circuit density and performance. Required skills: - PhD in Automatic Speech Recognition (preferred) or Machine Translation using deep neural networks - Knowledge of most widely used toolboxes/frameworks (pytorch, espnet) - Good programming skills in Python - Good communication skills (frequent interactions with hardware specialists) - Interest in hardware design will be a plus Selected references: S. Karita et al., 'A Comparative Study on Transformer vs RNN in Speech Applications,' 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), SG, Singapore, 2019, pp. 449-456, doi: 10.1109/ASRU46091.2019.9003750. Leila Ben Letaifa, Jean−Luc Rouas. Transformer Model Compression for End−to−End Speech Recognition on Mobile Devices. 2022 30th European Signal Processing Conference (EUSIPCO), Aug 2022, Belgrade, Serbia.
Leila Ben Letaifa, Jean−Luc Rouas. Fine-grained analysis of the transformer model for efficient pruning. 2022 International Conference on Machine Learning and Applications (ICMLA), Dec 2022, Nassau, Bahamas.
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6-30 | (2022-11-17) M2 internship offers LORIA - MULTISPEECH, Nancy France M2 internship offers LORIA - MULTISPEECH https ://team.inria.fr/multispeech/fr/ To apply, please send your CV and a short motivation letter directly to the supervisors of the corresponding offer. Offer 1 Contrastive Learning for Hate Speech Detection General information Supervisors Nicolas Zampieri, Irina Illina, Dominique Fohr Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Phone 03 54 95 84 06 Email fistname.lastname@loria.fr Office C 145 Motivation and context The United Nations defines hate speech as 'any type of communication through speech, writing or behavior, which denigrates a person or group based on who they are, i.e. their religion, ethnicity, nationality, or other identity factor.'. We are interested in hate speech posted on social networks. With the expansion of social networks (Twitter, Facebook, etc.), the number of messages posted every day has increased dramatically. It is very difficult and expensive to process the millions of content posted every day in order to remove hateful content. Thus, automatic methods are required to moderate the influx. Automatic hate speech detection is a difficult task in the field of natural language processing (NLP) [6]. With the appearance of transformer-based language models like BERT [3], new state-of-the-art models have emerged for hate speech detection like HateBERT [1]. Current NLP models rely strongly on efficient learning algorithms. We are particularly interested in one of them : contrastive learning. Contrastive learning is employed to learn an embedding space such that pairs of similar sentences have close representations. [5] provide a summary of different models based on contrastive learning in language processing. Goals and Objectives The goal of this internship is to study contrastive learning in the context of hate speech detection. We believe that using this methodology will make the models more effective. Our model learns to estimate whether two sentences have the same sentiment or not. Based on the first model, the intern will explore other approaches of contrastive learning, such as SimCSE [4] or Dual Contrastive Learning [2] models. The studied methods will be validated on several datasets to assess the robustness of the approach. In our team, we have several labeled corpora from social networks. The internship workplan is as follows : at the beginning the student will conduct a state-of-the-art study on recent developments in hate speech detection and contrastive learning in NLP. The student will implement the selected methods. Finally, the performance of the different implemented methods will be evaluated on several hate speech corpora and compared to the state-of-the-art. Required Skills The candidate should have an experience with Deep Learning, including a good practice in Python and an understanding of deep learning libraries like Keras, Pytorch or Tensorflow. Additional information The student intern will join the MULTISPEECH team. The team provides access to the computational resources (GPU, CPU and datasets) in order to carry out the research. References [1] Caselli, T., Basile, V., Mitrovic, J., & Granitzer, M. HateBERT : Retraining BERT for Abusive Language ´ Detection in English.. Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021) (pp. 17-25). ACL. doi :10.18653/v1/2021.woah-1.3. August 2021. [2] Chen, Q., Zhang, R., Zheng, Y., & Mao, Y. Dual Contrastive Learning : Text Classification via Label-Aware Data Augmentation.. https ://arxiv.org/abs/2201.08702. 2022. 2 [3] Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding.. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Volume 1 (Long and Short Papers), (pp. 4171-4186). 2022. [4] Gao, T., Yao, X., & Chen, D. SimCSE : Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. ACL. doi :10.18653/v1/2021.emnlp-main.552. 2021. [5] Rethmeier, N., & Augenstein, I. A Primer on Contrastive Pretraining in Language Processing : Methods, Lessons Learned and Perspectives.. https ://arxiv.org/abs/2102.12982. 2021. [6] Zampieri, N., Ramishc, C., Illina, I., & Fohr D. Identification of Multiword Expressions in Tweets for Hate Speech Detection.. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 202-210, 2022. European Language Resources Association. Offer 2 Diffusion-based Deep Generative Models for Audio-visual Speech Modeling General information Supervisors Mostafa SADEGHI, Romain SERIZEL Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Email mostafa.sadeghi@inria.fr,romain.serizel@loria.fr Motivation Recently, diffusion models have gained much attention due to their powerful generative modeling performance, in terms of both the diversity and quality of the generated samples [1]. It consists of two phases, where during the so-called forward diffusion process, input data are mapped into Gaussian noise by gradually perturbing the data. Then, during a reverse process, a denoising neural network is learned that removes the added noise at each step, starting from pure Gaussian noise, to eventually recover the original clean data. Diffusion models have found numerous successful applications, particularly in computer vision, e.g., text-conditioned image synthesis, outperforming previous generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and normalizing flows (NFs). Diffusion models have also been successfully applied to audio and speech signals, e.g., for audio synthesis [2] and speech enhancement [3]. Goal and objectives Despite their rapid progress and application extension, diffusion models have not yet been applied to audiovisual speech modeling. This task involves joint modeling of audio and visual modalities, where the latter concerns the lip movements of the speaker, as there is a correlation between what is being said and the lip movements. This joint modeling effectively incorporates the complementary information of visual modality for speech generation. Such a framework has already been established based on VAEs [4]. Given the great potential and advantages of diffusion models, in this project, we would like to develop a diffusion-based audio-visual generative modeling framework, where the generation of audio modality, i.e., speech, is conditioned on the visual modality, i.e., lip images, similarly to text-conditioned image synthesis. This might then serve as an efficient representation learning framework for downstream tasks, e.g., audio-visual speech enhancement (AVSE) [4]. Background in statistical signal processing, computer vision, machine learning, and deep learning frameworks (Python, PyTorch) are favored. Interested candidates should send an email to the supervisors with a detailed CV and transcripts. Work environment This master internship is part of the REAVISE project : 'Robust and Efficient Deep Learning based Audiovisual Speech Enhancement' (2023-2026) funded by the French National Research Agency (ANR). The general objective of REAVISE is to develop a unified AVSE framework that leverages recent methodological breakthroughs in statistical signal processing, machine learning, and deep neural networks in order to design a robust and efficient AVSE framework. The intern will be supervised by Mostafa Sadeghi (researcher, Inria) and Romain Serizel (associate professor, University of Lorraine), as members of the MULTISPEECH team, and will benefit from the research environment, expertise, and computational resources (GPU & CPU) of the team. References [1] L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, Y. Shao, W. Zhang, B. Cui, and M. H. Yang, Diffusion models : A comprehensive survey of methods and applications arXiv preprint arXiv :2209.00796, 2022. [2] Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, Diffwave : A versatile diffusion model for audio synthesis arXiv preprint arXiv :2009.09761, 2020. [3] Y. J. Lu, Z. Q. Wang, S. Watanabe, A. Richard, C. Yu, and Y. Tsao, Conditional diffusion probabilistic model for speech enhancement IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022. [4] M. Sadeghi, S. Leglaive, X. Alameda-Pineda, L. Girin, and R. Horaud, Audio-visual speech enhancement using conditional variational auto-encoders IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1788 ?1800, 2020. Offer 3 Efficient Attention-based Audio-visual Fusion Mechanisms for Speech Enhancement General information Supervisors Mostafa SADEGHI Romain SERIZEL Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Email mostafa.sadeghi@inria.fr romain.serizel@loria.fr Motivation Audiovisual speech enhancement (AVSE) is defined as the task of improving the quality and intelligibility of a noisy speech signal by utilizing the complementary information provided by the visual modality, i.e., lip movements of the speaker [1]. Visual modality is especially important in high-noise situations, as it is less affected by acoustic noise. Because of that, AVSE could be exploited in several practical applications, including hearing assistive devices. Numerous works have already studied the integration of visual modality with audio modality to improve the performance of speech enhancement. While the majority of audiovisual speech enhancement algorithms rely on deep neural networks and supervised learning, they require very large audiovisual datasets with diverse noise instances to have good generalization performance. A recently introduced AVSE approach is based on unsupervised learning [2,3], where during a training phase, the statistical distribution of clean speech is learned from a clean audiovisual dataset. This is done using a deep generative model, e.g. variational autoencoder (VAE) [4]. Then, at test (inference) time, the learned distribution is combined with a noise model to estimate the clean speech signal from the available noisy speech observations. Goal and objectives An important element of AVSE is audio-visual feature fusion, which should robustly and efficiently combine the two modalities. Current fusion mechanisms used for unsupervised AVSE are based on simple feature concatenation, which is not effective, as it treats the two feature streams on an equal basis. In fact, the audio modality usually contributes more than the visual modality, but in general, their contributions should be robustly balanced and weighted. In this project, we are going to develop efficient feature fusion modules based on attention models [5], which have proven very successful in different applications. The designed fusion module is supposed to robustly and efficiently incorporate the potentially different uncertainty (reliability) levels of the two modalities. We will then evaluate its effectiveness for AVSE. Background in statistical signal processing, probabilistic machine learning, optimization, and programming languages & deep learning frameworks (Python, PyTorch) are favored. Interested candidates should send an email to the supervisors with a detailed CV and transcripts. Work environment This master internship is part of the REAVISE project : 'Robust and Efficient Deep Learning based Audiovisual Speech Enhancement' (2023-2026) funded by the French National Research Agency (ANR). The general objective of REAVISE is to develop a unified AVSE framework that leverages recent methodological breakthroughs in statistical signal processing, machine learning, and deep neural networks in order to design a robust and efficient AVSE framework. The intern will be supervised by Mostafa Sadeghi (researcher, Inria) and Romain Serizel (associate professor, University of Lorraine), as members of the MULTISPEECH team, and will benefit from the research environment, expertise, and computational resources (GPU & CPU) of the team. References [1] D. Michelsanti, Z.-H. Tan, S.-X. Zhang, Y. Xu, M. Yu, D. Yu, and J. Jensen, An overview of deep-learningbased audio-visual speech enhancement and separation IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1368 ?1396, 2021. 5 [2] M. Sadeghi and X. Alameda-Pineda, Switching variational auto-encoders for noise-agnostic audio-visual speech enhancement IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021. [3] M. Sadeghi, S. Leglaive, X. Alameda-Pineda, L. Girin, and R. Horaud, Audio-visual speech enhancement using conditional variational auto-encoders IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1788 ?1800, 2020. [4] D. P. Kingma and M. Welling, An introduction to variational autoencoders Foundations and Trends in Machine Learning, 2019. [5] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need Advances in neural information processing systems, 2017. Offer 4 Multi-modal Stuttering Detection Using Self-supervised Learning General information Supervisors Shakeel Ahmad Sheikh, Slim Ouni Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Email fistname.lastname@loria.fr Office C 137 Motivation Stuttering is a neuro-developmental speech disorder that starts appearing when language, speech, and emotion supporting neural connections are changing quickly [2]. In standard stuttering therapy sessions, the speech pathologists or speech therapists either manually examine and analyze the person who stutter (PWS) speech or their recordings. In order to rectify the stuttering, the speech therapists carefully observe and monitor the patterns in speech utterances of PWS. However, this convention of stuttering detection is very time consuming and strenuous. It is also biased towards the subjective belief of speech language therapists. Thus, it is important to build stuttering detection interactive tools that provide impartial objective assessment, and can be utilized to tune and improve various ASR virtual assistants for stuttered speech. Deep learning has been used tremendously in domains like speech recognition [5], emotion detection [1], however, in stuttering domain, its application is limited. The acoustic cues embedded in the speech of PWS can be exploited by various deep learning methods in the detection of stuttering. Most of the existing stuttering detection techniques utilize spectral features such as spectrograms and MFCCs as an input representation of the stuttered speech [12, 11, 3]. The most common problem in the stuttering domain is the dataset issue. There are few stuttering datasets like UCLASS, FluencyBank, and SEP28K [3], which are small containing only a few dozens of speakers. While deep learning methods have shown substantial gains in domains like ASR, speaker verification, emotion detection, etc, however, the improvement in stuttering detection is very limited, most likely due to the miniature size of datasets. The common strategy in dealing with training on small datasets is to apply transfer learning, where the pre-trained model (trained first on some auxiliary task on a large dataset) is used to enhance the performance of the desired task, for which data is very scarce. The deep learning model trained on some auxiliary task can be fine-tuned by re-training, or replacing some of its last layers, or it can also be employed as a feature extractor for the desired task, that we are trying to address. Transfer learning methodology has been explored in various fields like ASR, emotion detection [8], etc. Recently, self-supervised learning has shown significant improvement in stuttering detection [11, 18, 17, 16]. Multimodal Stuttering Detection Stuttering can be characterized as an audio-visual problem. Cues are present both in the visual (e.g., head nodding, lip tremors, quick eye blinks, and unusual lip shapes) as well as in the audio modality [4]. This multimodal learning paradigm could be helpful in learning robust stutter-specific hidden representations across the cross-modality platform, and could also help in building robust automatic stuttering detection systems. Selfsupervised learning can also be exploited to capture acoustic stutter-specific representations based on guided video frames. As proposed by Shukla et al. [14], this framework could be helpful in learning stutter-specific features from audio signals guided by visual frames or vice versa. Altinkaya and Smeulders [15] recently presented the first audio-visual stuttered dataset which consists of 25 speakers (14 male, 11 female). They trained ResNet-based RNN (gated recurrent unit) on the audio-visual modality for the detection of block stuttering type. The main idea in this internship is to explore the impact of further self supervised learning in stuttering detection in combination with audio-visual setup. The goal of the proposed study is to develop and evaluate audio-visual based self supervised stuttering detection classifiers, that will be able to distinguish among several stutter classes. 1. Objective 1 : Lliterature survey by looking at the existing work in stuttering detection. 2. Objective 2 : Developing a pre-trained stuttering classifier based on self-supervised learning ; Some initial experiments would be carried out. We would explore the self supervised models such as wav2vec 2.0, a modified version of wav2vec [9], and their variants such as Unispeech, HuBERT, etc. We would use wav2vec 2.0 either as a feature extractor or just fine tune it by replacing the last few layers and adapt it for stuttering detection. 7 3. The experiments would be carried out on the newly developed French stuttering dataset. 4. Objective 3 : Carrying out the actual experiments and the impact of fine-tuning and pre-trained features would be analyzed on the raw stuttered embedded audio-visual stuttered samples. References [1] Mehmet Berkehan Ak Cay and Kaya Oguz L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, Y. Shao, W. Zhang, B. Cui, and M. H. Yang, Speech emotion recognition : Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers' Speech Communication, 116 (2020) pp.56- 76. [2] Smith, Anne and Weber, Christine How stuttering develops : The multifactorial dynamic pathways theory' Journal of Speech, Language, and Hearing Research, 60 (2017) pp.2483–2505. [3] Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch, Slim Ouni, Machine learning for stuttering identification : Review, challenges and future directions, Neurocomputing, 514 (2022), pp 385-402, [4] Guitar, Barry. Stuttering : An integrated approach to its nature and treatment. Lippincott Williams & Wilkins, 2013. [5] A. B. Nassif, I. Shahin, I. Attili, M. Azzeh and K. Shaalan, 'Speech Recognition Using Deep neural networks : A systematic review,' IEEE Access, vol. 7, pp. 19143-19165, 2019. [6] Latif, Siddique, Rajib Rana, Sara Khalifa, Raja Jurdak, Junaid Qadir, and Björn W. Schuller. 'Deep representation learning in speech processing : Challenges, recent advances, and future trends.' arXiv preprint arXiv :2001.00378 (2020). [7] Ning, Y., He, S., Wu, Z., Xing, C. and Zhang, L.J., 2019. A review of deep learning based speech synthesis. Applied Sciences, 9(19), p.4050. [8] Wang, Yingzhi, Abdelmoumene Boumadane, and Abdelwahab Heba. 'A fine-tuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding.' arXiv preprint arXiv :2111.02735 (2021). [9] Baevski, Alexei, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 'wav2vec 2.0 : A framework for self-supervised learning of speech representations.' Advances in Neural Information Processing Systems, 33 (2020) : 12449-12460. [10] Lea, Colin, Vikramjit Mitra, Aparna Joshi, Sachin Kajarekar, and Jeffrey P. Bigham. 'Sep-28k : A dataset for stuttering event detection from podcasts with people who stutter.' In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6798-6802. IEEE, 2021. [11] Sheikh, Shakeel A., Md Sahidullah, Slim Ouni, and Fabrice Hirsch. 'End-to-End and Self-supervised learning for ComParE 2022 stuttering sub-challenge.' In Proceedings of the 30th ACM International Conference on Multimedia, pp. 7104-7108. 2022. [12] Sheikh, Shakeel A., Md Sahidullah, Fabrice Hirsch, and Slim Ouni. 'Robust stuttering detection via multi-task and adversarial learning.' In 2022 30th European Signal Processing Conference (EUSIPCO), pp. 190-194. IEEE, 2022. [13] Ngiam, Jiquan, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y. Ng. 'Multimodal deep learning.' In ICML. 2011. [14] Shukla, Abhinav, Konstantinos Vougioukas, Pingchuan Ma, Stavros Petridis, and Maja Pantic. 'Visually guided self supervised learning of speech representations.' In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6299-6303. IEEE, 2020. [15] Altinkaya, Mehmet, and Arnold WM Smeulders. 'A dynamic, self supervised, large scale audiovisual dataset for stuttered speech.' In Proceedings of the 1st International Workshop on Multimodal Conversational AI, pp. 9-13. 2020. [16] Mohapatra, Payal, Akash Pandey, Bashima Islam, and Qi Zhu. 'Speech disfluency detection with contextual representation and data distillation.' In Proceedings of the 1st ACM International Workshop on Intelligent Acoustic Systems and Applications, pp. 19-24. 2022. [17] Grósz, Tamás, Dejan Porjazovski, Yaroslav Getman, Sudarsana Kadiri, and Mikko Kurimo. 'Wav2vec2- based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering.' In Proceedings of the 30th ACM International Conference on Multimedia, pp. 7026-7029. 2022. [18] Bayerl, Sebastian P., Dominik Wagner, Elmar Nöth, and Korbinian Riedhammer. 'Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0.' arXiv preprint arXiv :2204.03417 (2022). Offer 5 Dictionary learning for deep unsupervised speech separation General information Supervisors Paul Magron Mostafa Sadeghi Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Email paul.magron@inria.fr mostafa.sadeghi@inria.fr Office C 141 C 136 Motivation and context Speech separation consists in isolating the signals that correspond to each speaker from an acoustic mixture where several persons might be speaking. This task is an important preprocessing step in many applications such as hearing aids or vocal assistants based on automatic speech recognition. State-of-the-art separation systems rely on supervised deep learning, where a network is trained to predict the isolated speakers’ signals from their mixture [1, 2]. However, these approaches are costly in terms of training data and have a limited capacity to generalize to unseen speakers. Goal and objectives The goal of this internship is to design a fully unsupervised system for speech separation, which is more data-efficient than supervised approaches, and applicable to any mixture of speakers. To that end, we propose to combine variational autoencoders (VAEs) with dictionary models (DMs). DM consist in decomposing a given input matrix (usually : an audio spectrogram) as the product of two interpretable factors : a dictionary of spectra and a temporal activation matrix). This family of methods has been extensively researched before the era of deep learning [3], but it is limited since real-world audio spectrograms cannot be decomposed using such simple models. Therefore, we propose to leverage VAEs as a tool to learn a latent representation of the data which is regularized using DMs. Such a system can be cast as an instance of transform learning [4] : the key idea is to apply a (learned) transform to the data so that it better complies with a desirable property - here, decomposition on a dictionary. A first attempt was recently proposed and has shown promising results in terms of speech modeling [5], although it was using a fixed dictionary. This internship aims at extending this work by considering a system where both the VAE and the dictionary are learned jointly, and applying it to the task of speech separation. Once trained, the resulting system operates in three stages : (i) the (mixture) audio spectrogram is projected through the encoder into some latent space ; (ii) this latent representation is decomposed efficiently using a DM learning algorithm, which provides a latent feature for each speaker ; (iii) these latent features are passed through the decoder to retrieve a spectrogram for each speaker. Such a system is promising since it is fully unsupervised (it can be trained without knowledge of specific mixtures), it yields an interpretable decomposition of the latent representation, and it can serve as a basis for other applications (including speaker diarization, speech enhancement or voice conversion). A good practice in Python and basic knowledge about deep learning, both theoretical and practical (e.g., using PyTorch) are required. Some notions of audio/speech signal processing and machine learning is a plus. Work environment The trainee will be supervised by Paul Magron (Chargé de Recherche Inria) and Mostafa Sadeghi (Researcher, Inria Starting Faculty Position), and will benefit from the research environment and the expertise in audio signal processing of the MULTISPEECH team. This team includes many PhD students, post-docs, trainees, and permanent staff working in this field, and offers all the necessary computational resources (GPU and CPU, speech datasets) to conduct the proposed research. References [1] D. Wang and J. Chen, Supervised Speech Separation Based on Deep Learning : An Overview IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 10, pp. 1702-1726, 2018. [2] Y. Luo and N. Mesgarani, Conv-TasNet : Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 8, pp. 1256- 1266, 2019. [3] T. Virtanen, Monaural Sound Source Separation by Nonnegative Matrix Factorization With Temporal Continuity and Sparseness Criteria IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 3, pp. 1066-1074, 2007. [4] D. Fagot, H. Wendt and C. Févotte, Nonnegative Matrix Factorization with Transform Learning IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. [5] M. Sadeghi, and P. Magron, A Sparsity-promoting Dictionary Model for Variational Autoencoders Interspeech, 2022. Offer 6 Semantic latent space for expressive text-to-speech General information Supervisors Vincent Colotte, Slim Ouni Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Phone 03 54 95 20 74 Email vincent.colotte@loria.fr, slim.ouni@inria.fr Office C141 Motivation Over the last decades, text-to-speech synthesis (TTS) has reached good quality and intelligibility, and is now commonly used in information delivery services, as for instance in call center automation, and in navigation systems. In the past, the main goal when developing TTS systems was to achieve high intelligibility. The speech style was then typically a “reading style,” which resulted from the style of the speech data used to develop TTS systems (reading of a large set of sentences). Recent research on speech synthesis focuses now on expressive speech to obtain generated speech more expressive or spontaneous. Almost all systems are now based on neural network methods. Therefore, to tackle expressiveness integration in a network, as numerous recent works in neural networks, training and testing step pass through several steps with specific latent spaces to condition the network or to propose a latent representation to control the expressiveness. In stochastic processes, the explanation of such a numeric representation is still difficult to extract [1]. Moreover, the use of new representations as Word2Vec for textual material or Wav2Vec for audio signal shows that we can find a representation with implicit linguistic and semantic information. The need of explanation still remains. The internship will take place in this framework. Objectives and expected outcomes The goal of the proposed study is to investigate the information contained in a latent representation dedicated to expressive speech. Previous work dealt with Variational Autoencoder (VAE) approach to explore this dimension in the audiovisual domain [2] without emotion tag the latent representation retrieved the emotional information. In addition, [3] used several representations of acoustic expressiveness to condition a network to transfer an emotion from a speaker to another sentence of another speaker. Moreover, [5] had jointly used acoustic and textual expressiveness representation. The textual representation was based on SBERT approach. The internship work will consist to analyze latent representations of a TTS system (for instance Glow approach for audio speech or VAE for audio-visual speech). The second step will introduce semantic information by textual latent representation from simple tag [4], a description or the text itself. The objective is to jointly learn representations and analyze them to extract understanding for controlling the system. Additional information and requirements The internship will be carried out within the framework of the European project Humane-AI-Net. A good knowledge of Python and basic knowledge of neural network learning is required. References [1] Tits, N., Wang, F., Haddad, K.E., Pagel, V., Dutoit, T. Visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis, in Proc. Interspeech, 2019 [2] S. Dahmani, V. Colotte, V. Girard and S. Ouni Learning emotions latent representation with CVAE for TextDriven Expressive AudioVisual Speech Synthesis, in Neural Networks, Elsevier, 2021 [3] A. Kulkarni, V. Colotte, D. Jouvet, Analysis of expressivity transfer in non-autoregressive end-to-end multispeaker TTS systems, in Proc. Interspeech, 2022 [4] Kim, M., Cheon, S.J., Choi, B.J., Kim, J.J., Kim, N.S. Expressive Text-to-Speech Using Style Tag. Interspeech 2021 [5] Shin, Y., Lee, Y., Jo, S., Hwang, Y., Kim, T., Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS. Interspeech 2022.
Offer 7 Disentanglement in Speech Data for Privacy Needs General information Supervisors Emmanuel Vincent, Marc Tommasi Address LORIA, Campus Scientifique - BP 239, 54506 Vandœuvre-lès-Nancy Email emmanuel.vincent@inria.fr, marc.tommasi@inria.fr Motivation and context Large-scale collection, storage, and processing of speech data poses severe privacy threats [1]. Indeed, speech encapsulates a wealth of personal data (e.g., age and gender, ethnic origin, personality traits, health and socioeconomic status, etc.) which can be linked to the speaker’s identity via metadata or via automatic speaker recognition. Speech data may also be used for voice spoofing using voice cloning software. With firm backing by privacy legislations such as the European general data protection regulation (GDPR), several initiatives are emerging to develop and evaluate privacy preservation solutions for speech technology. These include voice anonymization methods [2] which aim to conceal the speaker’s voice identity without degrading the utility for downstream tasks, and speaker re-identification attacks [3] which aim to assess the resulting privacy guarantees, e.g., in the scope of the VoicePrivacy challenge series [4]. Goals and objectives The internship will tackle the objective of speech anonymization. Previous works have shown that simple adversarial approaches that aim at removing speaker identity from speech signals do not provide sufficient privacy guaranties [5]. An interpretation of this failure can be that adversaries were not strong enough. Moreover, there is no clear evidence that a transformation that removes speaker identity is informative enough to allow the reconstruction of intelligible speech signals. These observations raise a classical trade-off between privacy and utility that is essential in many privacy preservation scenarios. Instead of trying to remove speaker information, another option is to replace it by another one. To do so, a sub-objective is to disentangle speech signals, that is to isolate speech features that contribute to the success of speaker identification. Disentanglement is understood in this project as the process of embedding voice data in a new representation where different types of information (speaker identity, linguistic content, or even traits like age, gender or ethnicity) are separated and associated with disjoint sets of features. Variational autoencoders are supposed to naturally support disentanglement [6]. Additionally, variational approaches can also be used to make attackers stronger by introducing more diversity. Those two ways of improving adversarial approaches for learning a private representation of speech will be investigated. References [1] A. Nautsch, A. Jimenez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, A. Mtibaa, M. A. Abdelraheem, A. Abad, F. Teixeira, M. Gomez-Barrero, D. Petrovska, G. Chollet, N. Evans, T. Schneider, J.-F. Bonastre, B. Raj, I. Trancoso, and C. Busch. Preserving privacy in speaker and speech characterisation, in Computer Speech and Language, 2019 [2] B. M. L. Srivastava, M. Maouche, M. Sahidullah, E. Vincent, A. Bellet, M. Tommasi, N. Tomashenko, X. Wang, and J. Yamagishi. Privacy and utility of x-vector based speaker anonymization, in IEEE/ACM Transactions on Audio, Speech and Language Processing, 30 :2383–2395, 2022. [3] B. M. L. Srivastava, N. Vauquier, M. Sahidullah, A. Bellet, M. Tommasi, and E. Vincent. Evaluating voice conversion-based privacy protection against informed attackers, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. [4] N. Tomashenko, X. Wang, E. Vincent, J. Patino, B. M. L. Srivastava, P.-G. Noé, A. Nautsch, N. Evans, J. Yamagishi, B. O’Brien, A. Chanclu, J.-F. Bonastre, M. Todisco, and M. Maouche. The VoicePrivacy 2020 Challenge : Results and findings, Computer Speech and Language, 2022. [5] B. M. L. Srivastava, A. Bellet, M. Tommasi, and E. Vincent. Privacy-preserving adversarial representation learning in ASR : Reality or illusion ?, in Proc. Interspeech, 2019 [6] L. Girin, S. Leglaive, X. Bie, J. Diard, T. Hueber, and X. Alameda-Pineda. Dynamical Variational Autoencoders : A Comprehensive Review, in Foundations and Trends in Machine Learning, vol. 15, 2021 12
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6-31 | (2022-11-18) PhD studentships @ University of Edinburgh, Scotland, UK PHD STUDENTSHIPS IN SPEECH TECHNOLOGY, COMPUTATIONAL LINGUISTICS, AND COGNITIVE SCIENCE Institute for Language, Cognition and Computation The Institute for Language, Cognition and Computation (ILCC) at the University of Edinburgh invites applications for three-year PhD studentships starting in October 2023. ILCC is dedicated to the pursuit of basic and applied research on computational approaches to language, communication and cognition. Primary research areas include:
Approximately 10 studentships from a variety of sources are available, covering both maintenance at the research council rate of GBP 17,668 (2022/23 rates) per year and tuition fees. Awards increase every year, typically with inflation. Studentships are available for UK, EU, and non-EU nationals. For a list of academic staff at ILCC with research areas, and for a list of indicative PhD topics, please consult: http://web.inf.ed.ac.uk/ilcc/people/academic-senior-research-staff Details regarding the PhD programme and the application procedure can be found at: http://www.ed.ac.uk/informatics/postgraduate/research-degrees/phd There are TWO DEADLINES for applications to receive full consideration: round 1: 25th November 2022 We strongly recommend that non-UK applicants submit their applications in round 1, to maximise their chances of funding. Please direct inquiries to the PhD admissions team at ilcc-admissions@inf.ed.ac.uk. Please note that the 3-year ILCC PhD program is distinct from the UKRI Centre for Doctoral Training in Natural Language Processing, which offers a 4-year PhD with integrated study:
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6-32 | (2022-11-18) Poste d enseignant, IUT et ENSSAT, Lannion, France L’IUT de Lannion et l’ENSSAT à Lannion (22) recherchent chacun une enseignante-chercheuse ou un enseignant-chercheur sur contrat LRU à temps plein en Informatique pour le reste de l’année (jusque fin août 2023). L'intégration recherche peut se faire au sein de l'équipe EXPRESSION de l'IRISA, entre autres.
La date limite de candidature est très proche : 30/11 pour l’IUT et 11/12 pour l’ENSSAT, pour une prise de fonction vraisemblablement en janvier 2023. Les fiches de poste et les modalités de candidatures sont accessibles sur le site de l’université de Rennes 1 https://www.univ-rennes1.fr/nos-offres-demploi#p-126
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6-33 | (2022-11-20) Research position on speaker and text anonymization for medical applications @DFKI, Berlin, GE Research position on speaker and text anonymization for medical applications @DFKI, Berlin
We’re happy to announce a new research position in the field of speech- and text anonymization at German Research Center for Artificial Intelligence, Berlin, Germany. We’re looking for a full time Researcher or Junior Researcher level, and offer a 2 years contract with optional prolongation and PhD perspective.
The Speech and Language Technology Lab at DFKI Berlin is involved in numerous national as well as international research programmes and networks. We offer an interesting and flexible work environment as part of an innovative, international and enthusiastic team which coordinates and participates in national as well as European projects in the wider area of Language Technology.
Your tasks
Your qualifications
Your benefits
The German Research Center for Artificial Intelligence (DFKI) is Germany's leading business-oriented research institution in the field of innovative software technologies based on artificial intelligence methods. In the international scientific community, DFKI ranks among the most recognized 'Centers of Excellence' and currently is the biggest research center worldwide in the area of Artificial Intelligence and its application in terms of number of employees and the volume of external funds. The DFKI cooperates closely with national and international companies.
DFKI encourages applications from people with disability; DFKI intends to increase the proportion of female employees in the field of science and encourages women to apply for this position.
Application deadline: Dec 23 More details and link: https://jobs.dfki.de/en/internal/vacancy/en-researcher-m-w-d-in-506968.html
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6-34 | (2022-11-21) Postdoc @GIPSALab, Grenoble, France Offre de post-doc de 6 mois au GIPSA-lab, Grenoble, sur le contrôle gestuel temps-réel de l'intonation pour la suppléance vocale, dans le cadre du projet ANR GEPETO (Gestures and Pedagogy of Intonation). Informations généralesRéférence : UMR5216-CHRROM-022 MissionsCe post-doctorat fait partie du projet ANR GEPETO* (GEstures and PEdagogy of InTOnation), dont le but est d'étudier l'utilisation de gestes manuels par le biais d'interfaces humain-machine, pour la conception d'outils et méthodes permettant l'apprentissage du contrôle de l'intonation (mélodie) dans la parole. Activités- Prendre en main le système de conversion chuchotement-parole dans l'environnement Max/MSP CompétencesLes personnes n'ayant pas de compétence dans certains des domaines listés sont néanmoins encouragées à déposer une candidature. Contexte de travailLe Gipsa-lab est une unité de recherche commune CNRS, Grenoble-INP (Institut de Technologie de
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6-35 | (2022-11-21) Speech researcher @Vivoka, Metz, France
SPEECH RESEARCHER (M/F)
AboutVivokaVivoka is a global leader in voice Al technologies founded in 2015. Thanks to its VDK (Voice Development Kit), Vivoka offers an all-in-one solution that enables any company to create its own high performance, secure embedded/offline voice assistant in record time. Vivoka has won several innovation awards and has established leading partnerships with major players in the voice market. Vivoka has a portfolio of about 100 customers from all major industries and is pursuing its goal of bringing people closer to technology through voice.
MainmissionAspartofitsconstant evolution,Vivoka isfurther developingthe VoiceDevelopment Kitanditsrelatedprojects.Your mainmissionwillrevolve around creatingsolutions forsignalprocessingandmorespecifically speechprocessing.
Roles& Responsibilities
Job's Requirements
The advantages of the job
If you are interested in the position, send your documents to
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6-36 | (2022-11-21) NLP Researcher@Vivoka, Metz, France NLPRESEARCHER(M/F)
AboutVivokaVivoka is a global leader in voice Al technologies founded in 2015. Thanks to its VDK (Voice Development Kit), Vivoka offers an all-in-one solution that enables any company to create its own high-performance, secure embedded/offline voice assistant in record time. Vivoka has won several innovation awards and has established leading partnerships with major players in the voice market. Vivoka has a portfolio of about 100 customers from all major industries and is pursuing its goal of bringing people closer to technology through voice. MainmissionAspartofitsconstant evolution,Vivoka isfurther developingthe VoiceDevelopment Kitandits relatedprojects. Your mainmission willrevolve around creatingsolutions for NLPandmore specifically NLU.
Roles & Responsibilities
Job’s Requirements
The advantages of the job
If you are interested in the position, send your documents to
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6-37 | (2022-11-21) Machine Learning engineer@Vivoka, Metz, HFrance MACHINELEARNING ENGINEER (M/F)
AboutVivokaVivoka is a global leader in voice Al technologies founded in 2015. Thanks to its VDK (Voice Development Kit), Vivoka offers an all-in-one solution that enables any company to create its own highperformance, secure embedded/offline voice assistant in record time. Vivoka has won several innovation awards and has established leading partnerships with major players in the voice market. Vivoka has a portfolio of about 100 customers from all major industries and is pursuing its goal of bringing people closer to technology through voice.
MainmissionAspartof theR&Dteam,youwillbenchmark,develop,integrate anddeployour future embeddedMLtechnologiesfor the speechandnaturallanguageprocessing applications.
Roles & Responsibilities
Job's Requirements
R&D.
The advantages of the job
If you are interested in the position, send your documents to
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6-38 | (2022-11-22) Faculty position (Associate professor, tenure position) at Telecom Paris, France Faculty position (Associate professor, tenure position) at Telecom Paris in Machine-Learning for Social Computing.
Telecom Paris has a new permanent (tenure) faculty position (Associate Professor/ “Maître de conférences”) in the area of **machine learning for social computing**. Applicants from the following sub-research areas are welcome:
Important Dates
Context Social Computing team [1] - S²A (machine learning, statistics and signal processing) group [2] - LTCI (laboratoire de traitement et communication de l’information) [3] - Telecom Paris [4] .
Ecosystem Telecom Paris [4] is a founding member of the Institut Polytechnique de Paris (IP Paris), a world-class scientific and technological institution. Located at the Plateau de Saclay close to Paris-Saclay University, this Institution is a partnership between Ecole Polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris, Télecom SudParis, with HEC as a key partner. Regularly ranked as one of the best engineering schools in France, Télécom Paris is recognized for its excellent training, its very good employability rate with high salaries, its high-level research, and its very close proximity to companies. The THE (Times Higher Education) ranks Télécom Paris 2nd best French engineering school, 5th better French university, and 6th « best small university ». The newly created institution IP Paris was ranked in the top 50 best universities in the QS world university ranking. In the context of the Institut Polytechnique de Paris, the activities in Data Science and AI of the team benefit from the center Hi!Paris (https://www.hi-paris.fr), offering seminars, workshops, and fundings through calls for project
Main missions/Research activities
Main missions/Teaching activities Participate in teaching activities at Telecom Paris and its partner academic institutions (as part of joint Master programs), especially in natural language processing, speech processing, machine learning, and Data Science, including life-long training programs (e.g. the local “Mastères Spécialisés”)
Candidate profile As a minimum requirement, the successful candidate will have:
NOTE: The candidate does *not* need to speak French to apply, just to be willing to learn the language (teaching will be mostly given in English)
Other skills expected include: • Capacity to work in a team and develop good relationships with colleagues and peers • Excellent writing and pedagogical skills
More about the position • Place of work: Saclay (Paris outskirts)
How to apply? Applications must be submitted via one of the following websites: French Version: English Version:
Applicants should submit a single PDF file that includes: - cover letter, - curriculum vitae, - statements of research and teaching interests (4 pages) - three publications - contact information for two references
Contacts: == please do not hesitate to directly contact us before applying == Chloé Clavel (Coordinator of the Social Computing team) Stéphan Clémençon (Head of the S²A group) Florence d’Alché-Buc (Head of the IDS department)
[3] https://www.telecom-paris.fr/fr/lecole/departements-enseignement-recherche/image-donnees-signal [5] https://www.telecom-paris.fr/en/home
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6-39 | (2022-11-23) Postdoc for speech-based affective computing , King's college, London I am looking for a post-doc for speech-based affective computing and multimodal mHealth applications. For full details, see: https://jobs.kcl.ac.uk/gb/en/job/058426/Research-Fellow-in-Data-Science-for-Mobile-Health-mHealth Dr. Nicholas Cummins Institute of Psychiatry, Psychology & Neuroscience
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6-40 | (2022-11-24) Internship @UMRAE, Strasbourg, France UMRAE-INRIA PROPOSITION DE STAGE 2022-2023
Sujet de stage Nouveaux algorithmes pour le diagnostic acoustique de salle automatisé Niveau recommandé ☒Master (M2) ☐Master (M1) ☐Ingénieur ☐Licence ☐Bac + 2 Compétences requises Acoustique des salles, Méthodes d’optimisation, Traitement du signal, Apprentissage automatique Des connaissances en Python, Matlab serait un plus. Master 2 (acoustique, informatique, traitement du signal…) Introduction générale
Les nuisances sonores sont citées comme première source de gêne par les populations et constituent un enjeu sanitaire et social important. Dans les bâtiments, la gêne est souvent liée à une mauvaise qualité acoustique des salles due à une réverbération trop importante (cantine, piscine, crèche…). Dans le cadre de la réhabilitation acoustique des salles, la proposition d’une solution nécessite une bonne connaissance des caractéristiques géométriques et acoustiques de l’existant. Pour estimer certains paramètres inconnus (ex : absorption d’un plafond inconnu), les acousticiens de terrain s’appuient sur des mesures in situ du champ sonore et sur des modèles acoustiques numériques (ou analytique) dont ils calent de façon itérative les paramètres de façon à retrouver la valeur du champ sonore mesurée. Le processus complet d’un diagnostic est donc long, coûteux et parfois imprécis selon les modèles utilisés. Face à ce constat, le développement de méthodes dites inversespermettant de remonter automatiquement aux paramètres acoustiques d’intérêt à partir de la mesure constituerait une percée majeure pour l’acoustique du bâtiment, ouvrant la voie au développement d’outils plus simples, plus rapides et plus fiables à destination des acousticiens.
Sujet
Notre sujet, portant sur le développement de méthodes inverses en acoustique du bâtiment via des méthodes d’optimisation, de traitement du signal audio et/ou d’apprentissage automatique vient compléter la palette d’outils de prévision de champ sonore déjà existant. Par ailleurs, il vient aussi rompre l’herméticité existante entre le monde de l’audio et celui de l’acoustique, se traduisant par des conférences et journaux distincts. Des travaux préliminaires conduits par l’UMRAE et l’Inria, basés sur la réponse impulsionnelle de la salle (RIR ou Room Impulse Response), ont clairement montré qu’une application directe des approches d’optimisation (ainsi que d’approches d’apprentissage automatique) existantes dans d’autres domaines ne pouvait suffire pour résoudre notre problème. Ces approches doivent être adaptées au cas spécifique de l’acoustique. A ce jour, pour des conditions idéalisées (microphones et sources omnidirectionnels, salle rectangulaire, absorption plutôt faible des parois…), nos travaux ont montré qu’il est possible de « retrouver » au sein de la RIR l’absorption des parois et de reconstruire la géométrie de la salle et ce, sans connaissance a priori sur la position de la source sonore, des microphones. Le/la candidat(e), entouré(e) de ses encadrants, viendra en renfort de deux doctorants travaillant sur ces méthodes inverses. Pour cela, il/elle devra prendre en main l’une des méthodes d’optimisation déjà mises en place (algorithmes itératifs Ransac, Sliding Franck Wolfe, Méthode des solutions fondamentales, réseaux de neurones…) ainsi que le modèle théorique spécifiquement retenu pour ces travaux d’optimisation pouvant être exprimé dans le domaine temporel, de Fourier ou sur une décomposition en harmoniques sphériques. Plusieurs pistes de travail sont ensuite possibles, suivant les affinités du candidat et après discussion avec ses encadrants. Il pourra s’intéresser au modèle théorique de référence, par exemple, en l’utilisant dans l’un des domaines (temporel, Fourier ou Harmoniques sphériques). Il pourra aussi chercher à l’améliorer en y intégrant par exemple la directivité des sources et microphones, ou la diffusion des parois. En parallèle, le candidat pourra aussi s’intéresser à affiner les méthodes d’optimisation retenues pour le cas de l’acoustique, voire de proposer un autre algorithme d’optimisation utilisé dans un autre domaine de la physique. Pour finir, afin de valider ses travaux, le candidat aura à sa disposition des RIRs simulées avec des outils numériques de référence, mais aussi des RIRs mesurées notamment dans le cadre d’un projet de recherche mené conjointement avec l’Institut de Recherche en Coordination Acoustique/Musique (IRCAM Paris).
Informations générales
Lieu et durée du stage Le stage aura lieu à Strasbourg au sein des locaux du Cerema (11 rue Jean Mentelin – 67200 Straabourg). Le stage est prévu pour une durée de 4-6 mois.
Encadrants
Antoine Deleforge, Chargé de Recherche Inria, Equipe Multispeech, 615 rue du jardin botanique, 54600 Villiers lès Nancy. Pour des raisons pratiques, Antoine Deleforge est physiquement au sein de l’agence de Strasbourg 2 jours par semaine. https://members.loria.fr/ADeleforge/ ; https://team.inria.fr/multispeech/ ; https://www.inria.fr/fr
Cédric Foy, Chargé de Recherche UMRAE, Cerema, Univ. Gustave Eiffel, 11 rue Jean Menteli, 67200 Strasbourg https://www.cerema.fr/fr ; https://www.umrae.fr/ ; https://twitter.com/umrae_lab
Bibliographie T. Sprunck, K. Chahdi, C. Foy, E. Franck, A. Deleforge, Reconstruction de la forme d’une pièce par super-résolution à l’aide de réponses impulsionnelles, 16ème Congrès Français d'Acoustique, Marseille, France, 11-15 avr. 2022.
S. Dilungana, A. Deleforge, C. Foy, S. Faisan, Estimation jointe des profils d’absorption des parois d’une salle à partir de réponses impulsionnelles, 16ème Congrès Français d'Acoustique, Marseille, France, 11-15 avr. 2022.
S. Dilungana, A. Deleforge, C. Foy, S. Faisan, Geometry-Informed estimation of surface absorption profiles from impulses responses, Eusipco, 30th European Signal Processing Conference, Belgrade, Serbia, 2022.
T. Sprunck, Y. Privat, C. Foy, A. Deleforge, Gridless 3D Recovery if Images Sources from Room Impulse Responses, preprint, 2022, https://arxiv.org/abs/2208.14017
S. Dilungana, A. Deleforge, C. Foy, and S. Faisan, Learning-based estimation of individual absorption profiles from a single room impulse response with known positions of source, sensor and surfaces. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings, vol. 263, No. 1, pp. 5623-5630, 2021.
https://jtav.ifsttar.fr/fileadmin/contributeurs/JTAV/2022/JTAV2022_foyetcoll.pdf
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6-41 | (2022-12-05) Permanent academic post in Speech Technology@ University of Edinburgh, Scotland, UK The School of Informatics at the University of Edinburgh is recuiting for a permanent academic post in Speech Technology. The appointment will be at Lecturer or Reader grade (equivalent to US Assistant Professor/Associate Professor). You will contribute to research and teaching in the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition, and Computation (ILCC). There is extensive scope to collaborate with other Institutes and Schools within the University.
The successful candidate will have (or be near to completing) a PhD, an established research agenda and the enthusiasm and ability to undertake original research, to lead a research group, and to engage with teaching and academic supervision. We are seeking current and future leaders in the field who are able to forge new collaborations both within the field and across disciplines. We are particularly looking for a candidate with potential to extend the breadth of our research beyond our traditional core strengths in speech recognition and synthesis towards emerging applications, for example in spoken dialogue systems; spoken language understanding; healthcare and assistive technology applications; explainable speech processing; human computer interaction; or autonomous systems. For more details, including how to apply, view the full advert at https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/job/5973 Applications close on 12 January. The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.
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6-42 | (2022-12-08) Ph.D. Position in Cognitive Neuroscience@ GIPSA, Grenoble, France
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6-43 | (2023-12-05) Advanced Selective Mutual Learning for audio source separation @SteelSeries France R&D team (former Nahimic R&D team), France Advanced Selective Mutal Learning for audio source separation Master internship, Lille (France), 2022 Advisors — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com — Pierre Biret, R&D Engineer, pierre.biret@steelseries.com Company description About GN Group GN was founded 150 years ago with a truly innovative and global mindset. Today, we honour that legacy with world-leading expertise in the human ear, sound and video processing, wireless technology, miniaturization and collaborations with leading technology partners. GN’s solutions are marketed by the brands ReSound, Beltone, Interton, Jabra, BlueParrott, SteelSeries and FalCom in 100 countries. The GN Group employs 6,500 people and is listed on Nasdaq Copenhagen (GN.CO). About SteelSeries SteelSeries is the worldwide leader in gaming and esports peripherals focused on premium quality, innovation, and functionality. SteelSeries’ family of professional and gaming enthusiasts are the driving force behind the company and help influence, design, and craft every single accessory and the brand’s software ecosystem, SteelSeries GG. In 2020, SteelSeries acquired Nahimic, the leader in 3D sound solutions for gaming. We are currently looking for a machine learning / audio signal processing intern to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team). Internship subject Audio source separation consists in extracting the different sound sources present in an audio signal, in particular by estimating their frequency distributions and/or spatial positions. Many applications are possible from karaoke generation to speech denoising. In 2020, our separation approaches [1, 2] were equaling the state of the art [3, 4] on a music separation task. Since then our speech denoising product has hit the market [5] and the team continue to explore many tracks of improvements (see for instance the following project [6, 7]). Selective Mutual Learning (SML) Mutual learning (ML) [8] is a general idea related to knowledge distillation (KD) [9] where a group of untrained lightweight networks simultaneously learn and share knowledge to perform tasks together during training. The specificity of Selective Mutual Learning [10] is that the high-confidence predictions are used to guide the remaining network while the low-confidence predictions are ignored. This helps removing poor predictions of the networks during sharing knowledge. It can be noticed that the knowledge sharing is operated via loss functions that take into account the prediction of the other networks. The approach is simple and already shows benefits compared to KD and ML for boosting the performance of the networks for speech separation. Research axes The intern will be able to use existing internal trainsets and already implemented network architectures, which will facilitate drawing unbiased comparisons to our baseline approach. After re-implementing the SML approach, here is a list of possible axes of improvement of the SML approach : — tune the confidence factor (hyper-parameter c in [10]) to fit our speech denoising baseline (DNN and trainset) — extend and test the SML approach to more than 2 networks — adapt the SML loss formula to incorporate our internal loss (description upon request) 1 — additional tests with — new or already implemented networks : TasNet [11] ,E3net [12], DPRNN [13], transformer [1]) — various trainset (music separation , speech separation, ... ) Skills Who are we looking for ? Preparing an engineering degree or a master’s degree, you preferably have knowledge in the development and implementation of advanced machine learning algorithms. Digital audio signal processing skills is a plus. Whereas not mandatory, notions in the following additional various fields would be appreciated : Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Python, Pytorch, Keras, Tensorflow, Matlab. - Voice recognition, voice command. - Computer programming and development : Max/MSP, C/C++/C#. - Audio editing software : Audacity, Adobe Audition, etc. - Scientific publications and patent applications. - Fluent in English and French. - Demonstrate intellectual curiosity. Références [1] I. Alaoui Abdellaoui et N. Souviraà-Labastie. « Blending the attention mechanism in TasNet ». working paper or preprint. Nov. 2020. [2] E. Pierson Lancaster et N. Souviraà-Labastie. « A frugal approach to music source separation ». working paper or preprint. Nov. 2020. [3] F.-R. Stöter, A. Liutkus et N. Ito. « The 2018 signal separation evaluation campaign ». In : International Conference on Latent Variable Analysis and Signal Separation. Springer. 2018, p. 293-305. [4] N. Takahashi et Y. Mitsufuji. « Multi-scale Multi-band DenseNets for Audio Source Separation ». In : 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). 29 juin 2017. arXiv : 1706.09588. [5] ClearCast AI Noise Canceling - Promotion video. https : / / www . youtube . com / watch ? v = RD4eXKEw4Lg. [6] M. Vial et N. Souviraà-Labastie. Learning rate scheduling and gradient clipping for audio source separation. Rapp. tech. SteelSeries France, déc. 2022. [7] The torchcustoml rschedulersGitHubrepository. https : / / github . com / SteelSeries / torch _ custom_lr_schedulers. [8] Y. Zhang et al. « Deep mutual learning ». In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 4320-4328. [9] G. Hinton, O. Vinyals, J. Dean et al. « Distilling the knowledge in a neural network ». In : arXiv preprint arXiv :1503.02531 2.7 (2015). [10] H. M. Tan et al. « Selective Mutual Learning : An Efficient Approach for Single Channel Speech Separation ». In : ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, p. 3678-3682. [11] Y. Luo et N. Mesgarani. « TasNet : time-domain audio separation network for real-time, singlechannel speech separation ». In : arXiv :1711.00541 [cs, eess] (1er nov. 2017). 4*. [12] M. Thakker et al. « Fast Real-time Personalized Speech Enhancement : End-to-End Enhancement Network (E3Net) and Knowledge Distillation ». In : arXiv preprint arXiv :2204.00771 (2022). [13] Y. Luo, Z. Chen et T. Yoshioka. « Dual-path rnn : efficient long sequence modeling for timedomain single-channel speech separation ». In : ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2020, p. 46-50.
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6-44 | (2023-12-05) Personalized speech enhancement Master internship, Lille (France) @SteelSeries France R&D team (former Nahimic R&D team), France Personalized speech enhancement Master internship, Lille (France), 2022
Advisors — Damien Granger, R&D Engineer, damien.granger@steelseries.com — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com — Lucas Dislaire, R&D Engineer, lucas.dislaire@steelseries.com Company description About GN Group GN was founded 150 years ago with a truly innovative and global mindset. Today, we honour that legacy with world-leading expertise in the human ear, sound and video processing, wireless technology, miniaturization and collaborations with leading technology partners. GN’s solutions are marketed by the brands ReSound, Beltone, Interton, Jabra, BlueParrott, SteelSeries and FalCom in 100 countries. The GN Group employs 6,500 people and is listed on Nasdaq Copenhagen (GN.CO). About SteelSeries SteelSeries is the worldwide leader in gaming and esports peripherals focused on premium quality, innovation, and functionality. SteelSeries’ family of professional and gaming enthusiasts are the driving force behind the company and help influence, design, and craft every single accessory and the brand’s software ecosystem, SteelSeries GG. In 2020, SteelSeries acquired Nahimic, the leader in 3D sound solutions for gaming. We are currently looking for a machine learning / audio signal processing intern to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team). Internship subject Audio source separation consists in extracting the different sound sources present in an audio signal, in particular by estimating their frequency distributions and/or spatial positions. Many applications are possible from karaoke generation to speech denoising. In 2020, our separation approaches [1, 2] were equaling the state of the art [3, 4] on a music separation task. Since then our speech denoising product has hit the market [5] and the team continue to explore many tracks of improvements (see for instance the following project [6, 7]). Speech related audio source separation task Speech enhancement[8] or speech denoising usually refers to the task where the signal of interest is one intelligible speaker drown in additive noise. Speech separation [9] (sometime speaker separation) refers to the task of separately retrieving multiple unknown speakers usually not drown in additive noise. In the case of Personalized Speech Enhancement (also called target voice separation or target speaker extraction), the signal of interest is a speaker but a known speaker. This opens to (1) potentially better speech enhancement performances and also (2) focus on a particular speaker where speech enhancement would have kept all intelligible speakers. Personalized speech enhancement The research area is really recent and has just been added last year to the DNS challenge 1 [10]. VoiceFilter [11] seems to be the first article to tackle the problem of Personalized speech enhancement. 1. It can be noticed that the DNS challenge tasks [10] are defined as being real-time with a 40ms constraint on the latency (mainly composed of the look-ahead). 1 It uses two separately-trained neural networks : one discrimination network that produces speaker-specific embeddings from reference utterances of the target speaker ; and one “main” network, that performs the actual speech enhancement by taking as input both the corrupted utterance and the target speaker embeddings. The approach has now been outperformed, e.g., [12], while the two-step approach tends to prevail in the literature [13, 14, 15] : firstly one needs to learn a target speaker representation during an enrollment phase for instance by means of speaker embeddings, such as x-vectors or d-vectors [12], secondly incorporate this results in the neural network that will learn to extract this target speaker’s speech. However, it can be noticed that two steps does not necessarily means 2 networks, for instance a jointly trained 4-stage network is proposed in [15]. Axis of research The objective of the internship is to address one or several of the following targets : — Firstly, a baseline framework needs to be set up. It will require : — A dataset tailored for the task : the available datasets in the scientific community does not completely fulfill the requirements for SteelSeries products (description upon request). Conversely, our current datasets partly lacks speaker information. Hence, one would need to opt for the best solution or tradeoff combination. — A speaker embedding baseline with the assumption that the signal captured during the enrollment is “clean”, i.e. only contains the signal of interest (or at least with no second speaker). — A speech enhancement model with speaker embeddings. The intern could for instance re-use our implementation (currently without speaker embeddings) of E3net [14]. — Secondly, once a first baseline has been trained, the candidate could benchmark on different scenarii (signal-to-noise ratio during enrollment, signal ratio between speakers and effect of additional noise, various and mixed language). The Target Speaker Over-Suppression metric could potentially be used (description in [12]), as well as DNS standard metrics. This could lead the candidate to work on one of the following items to improve the baseline framework on its identified weaknesses : — Testing various speaker embeddings and ways or positions of integration into the networks — The separate training of the speaker-encoding network has been found to work better than joint training [16, 17, 15] (multi-task learning being often hard to tune). However, this would need to be reassessed with the final chosen architecture. — More effective enrollment strategies [18, 19] could be chosen and adapted to SteelSeries use cases. — Implementing loss functions suitable for the separation task (Step 2) could also be of interest, for instance following ideas in [20] or by adapting our internal loss. Skills Who are we looking for ? Preparing an engineering degree or a master’s degree, you preferably have knowledge in the development and implementation of advanced machine learning algorithms. Digital audio signal processing skills is a plus. Whereas not mandatory, notions in the following additional various fields would be appreciated : Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Python, Pytorch, Keras, Tensorflow, Matlab. - Voice recognition, voice command. - Computer programming and development : Max/MSP, C/C++/C#. - Audio editing software : Audacity, Adobe Audition, etc. - Scientific publications and patent applications. - Fluent in English and French. - Demonstrate intellectual curiosity. 2 Références [1] I. Alaoui Abdellaoui et N. Souviraà-Labastie. « Blending the attention mechanism in TasNet ». working paper or preprint. Nov. 2020. [2] E. Pierson Lancaster et N. Souviraà-Labastie. « A frugal approach to music source separation ». working paper or preprint. Nov. 2020. [3] F.-R. Stöter, A. Liutkus et N. Ito. « The 2018 signal separation evaluation campaign ». In : International Conference on Latent Variable Analysis and Signal Separation. Springer. 2018, p. 293-305. [4] N. Takahashi et Y. Mitsufuji. « Multi-scale Multi-band DenseNets for Audio Source Separation ». In : 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). 29 juin 2017. arXiv : 1706.09588. [5] ClearCast AI Noise Canceling - Promotion video. https : / / www . youtube . com / watch ? v = RD4eXKEw4Lg. [6] M. Vial et N. Souviraà-Labastie. Learning rate scheduling and gradient clipping for audio source separation. Rapp. tech. SteelSeries France, déc. 2022. [7] The torchcustoml rschedulersGitHubrepository. https : / / github . com / SteelSeries / torch _ custom_lr_schedulers. [8] DNS challenge on the paperswithcode website. https://paperswithcode.com/sota/speechenhancement-on-deep-noise-suppression. [9] Speech separation task referenced on the paperswithcode website. https://paperswithcode.com/ task/speech-separation. [10] H. Dubey et al. « Icassp 2022 deep noise suppression challenge ». In : ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, p. 9271- 9275. [11] Q. Wang et al. « Voicefilter : Targeted voice separation by speaker-conditioned spectrogram masking ». In : arXiv preprint arXiv :1810.04826 (2018). [12] S. E. Eskimez et al. « Personalized speech enhancement : New models and comprehensive evaluation ». In : ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, p. 356-360. [13] R. Giri et al. « Personalized percepnet : Real-time, low-complexity target voice separation and enhancement ». In : arXiv preprint arXiv :2106.04129 (2021). [14] M. Thakker et al. « Fast Real-time Personalized Speech Enhancement : End-to-End Enhancement Network (E3Net) and Knowledge Distillation ». In : arXiv preprint arXiv :2204.00771 (2022). [15] C. Xu et al. « Spex : Multi-scale time domain speaker extraction network ». In : IEEE/ACM transactions on audio, speech, and language processing 28 (2020), p. 1370-1384. [16] K. Žmolıková et al. « Learning speaker representation for neural network based multichannel speaker extraction ». In : 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE. 2017, p. 8-15. [17] M. Delcroix et al. « Single channel target speaker extraction and recognition with speaker beam ». In : 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. 2018, p. 5554-5558. [18] H. Sato et al. « Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations ». In : arXiv preprint arXiv :2206.08174 (2022). [19] X. Liu, X. Li et J. Serrà. « Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation ». In : arXiv preprint arXiv :2210.12635 (2022). [20] H. Taherian, S. E. Eskimez et T. Yoshioka. « Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation ». In : arXiv preprint arXiv :2211.02944 (2022).
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6-45 | (2022-12-05) Real time speaker separation Master internship, Lille (France), 2022@SteelSeries France R&D team (former Nahimic R&D team), France Real time speaker separation Master internship, Lille (France), 2022 Advisors — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com — Damien Granger, R&D Engineer, damien.granger@steelseries.com Company description About GN Group GN was founded 150 years ago with a truly innovative and global mindset. Today, we honour that legacy with world-leading expertise in the human ear, sound and video processing, wireless technology, miniaturization and collaborations with leading technology partners. GN’s solutions are marketed by the brands ReSound, Beltone, Interton, Jabra, BlueParrott, SteelSeries and FalCom in 100 countries. The GN Group employs 6,500 people and is listed on Nasdaq Copenhagen (GN.CO). About SteelSeries SteelSeries is the worldwide leader in gaming and esports peripherals focused on premium quality, innovation, and functionality. SteelSeries’ family of professional and gaming enthusiasts are the driving force behind the company and help influence, design, and craft every single accessory and the brand’s software ecosystem, SteelSeries GG. In 2020, SteelSeries acquired Nahimic, the leader in 3D sound solutions for gaming. We are currently looking for a machine learning / audio signal processing intern to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team). Internship subject Audio source separation consists in extracting the different sound sources present in an audio signal, in particular by estimating their frequency distributions and/or spatial positions. Many applications are possible from karaoke generation to speech denoising. In 2020, our separation approaches [1, 2] were equaling the state of the art [3, 4] on a music separation task. Since then our speech denoising product has hit the market [5] and the team continue to explore many tracks of improvements (see for instance the following project [6, 7]). Real time speaker separation This internship targets speaker separation which is formalized in the scientific community as the task of separately retrieving a given number of speech/speaker signals from a monaural mixture signal. Most of the scientific challenges [8] compare offline (not real-time) approaches. The objective of the internship is to address the following targets (more or less ordered) : — Based on our current speech denoising trainsets, the candidate will create a trainset for the speaker separation task that match the same in-house requirement. Indeed, most of the available datasets in the scientific community lack quantity, audio quality of the groundtruths, high sampling rate, diversity of speakers/noise type. In addition, for the SteelSeries use cases, the overlap in time of the different speech sources might be lower than in the scenarii used by the scientific community and it statistical distribution will need to be well identified/defined. — Once our offline and online baseline algorithm have been trained on such a trainset, the candidate could benchmark on different scenarii (number of speaker, signal ratio between speakers, effect of additional noise, various and mixed languages) to potentially fulfill the weakness of the trainset. — The first subjective listening could bring the candidate to design complementary metrics, for instance representing false positive in speaker attribution or representating the statistics about the time needed by real-time DNN to correctly attribute a signal to the correct speaker after some silence. 1 — While all the above could be done using state-of-the-art loss functions, the candidate could also adapt our internal loss to be permutation invariant [9]. — The scientific community is very active in proposing new DNN architectures (offline [10, 8] and online [11, 12]. The candidate could also re-implement or propose her/his own architecture. In particular, a multi-task approach where the DNN also outputs the number of active speakers would be of great interest. Skills Who are we looking for ? Preparing an engineering degree or a master’s degree, you preferably have knowledge in the development and implementation of advanced machine learning algorithms. Digital audio signal processing skills is a plus. Whereas not mandatory, notions in the following additional various fields would be appreciated : Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Python, Pytorch, Keras, Tensorflow, Matlab. - Voice recognition, voice command. - Computer programming and development : Max/MSP, C/C++/C#. - Audio editing software : Audacity, Adobe Audition, etc. - Scientific publications and patent applications. - Fluent in English and French. - Demonstrate intellectual curiosity. Références [1] I. Alaoui Abdellaoui et N. Souviraà-Labastie. « Blending the attention mechanism in TasNet ». working paper or preprint. Nov. 2020. [2] E. Pierson Lancaster et N. Souviraà-Labastie. « A frugal approach to music source separation ». working paper or preprint. Nov. 2020. [3] F.-R. Stöter, A. Liutkus et N. Ito. « The 2018 signal separation evaluation campaign ». In : International Conference on Latent Variable Analysis and Signal Separation. Springer. 2018, p. 293-305. [4] N. Takahashi et Y. Mitsufuji. « Multi-scale Multi-band DenseNets for Audio Source Separation ». In : 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). 29 juin 2017. arXiv : 1706.09588. [5] ClearCast AI Noise Canceling - Promotion video. https : / / www . youtube . com / watch ? v = RD4eXKEw4Lg. [6] M. Vial et N. Souviraà-Labastie. Learning rate scheduling and gradient clipping for audio source separation. Rapp. tech. SteelSeries France, déc. 2022. [7] The torchcustoml rschedulersGitHubrepository. https : / / github . com / SteelSeries / torch _ custom_lr_schedulers. [8] Speech separation task referenced on the paperswithcode website. https://paperswithcode.com/ task/speech-separation. [9] X. Liu et J. Pons. « On permutation invariant training for speech source separation ». In : ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2021, p. 6-10. [10] Music separation task referenced on the paperswithcode website. https://paperswithcode.com/ sota/music-source-separation-on-musdb18. [11] DNS challenge on the paperswithcode website. https://paperswithcode.com/sota/speechenhancement-on-deep-noise-suppression. [12] H. Dubey et al. « Icassp 2022 deep noise suppression challenge ». In : ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, p. 9271-9275
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6-46 | (2022-11-05)Audio detection for gaming Master internship, Lille (France), 2022@SteelSeries France R&D team (former Nahimic R&D team), France Audio detection for gaming Master internship, Lille (France), 2022 Advisors — Damien Granger, R&D Engineer, damien.granger@steelseries.com — Nathan Souviraà-Labastie, R&D Engineer, PhD, nathan.souviraa-labastie@steelseries.com Company description About GN Group GN was founded 150 years ago with a truly innovative and global mindset. Today, we honour that legacy with world-leading expertise in the human ear, sound and video processing, wireless technology, miniaturization and collaborations with leading technology partners. GN’s solutions are marketed by the brands ReSound, Beltone, Interton, Jabra, BlueParrott, SteelSeries and FalCom in 100 countries. The GN Group employs 6,500 people and is listed on Nasdaq Copenhagen (GN.CO). About SteelSeries SteelSeries is the worldwide leader in gaming and esports peripherals focused on premium quality, innovation, and functionality. SteelSeries’ family of professional and gaming enthusiasts are the driving force behind the company and help influence, design, and craft every single accessory and the brand’s software ecosystem, SteelSeries GG. In 2020, SteelSeries acquired Nahimic, the leader in 3D sound solutions for gaming. We are currently looking for a machine learning / audio signal processing intern to join the R&D team of SteelSeries’ Software & Services Business Unit in our French office (former Nahimic R&D team). Internship subject This internship targets the detection of a known signal in an audio scene (containing multiple signals). For instance, some signs and feedbacks in video games are always the same audio signal while the rest of the audio scene is changing. The current internal implementation is based on a legacy state of the art music identification system [1, 2] The objective of the internship is to address one or multiple of the following targets : — Agnostic to the source type (speech, music, audio gaming event ...), indeed the current approach is designed for music — Enable the handling of shorter target signal — Robustness to various overlapping audio noise type from the audio scene — Robustness to level variation over time (in the case of moving audio sources) — Explore the effect of having multi-channel signals as input, summing the channels might help to identify moving sound but potentially with the drawback of lowering the signal-to-noise ratio — Improvement of the above-mentioned aspects by adapting the approach to the use of a smaller dictionary of target signals (not millions like in the case of musics) Machine learning is the expected track, and in particular, pre-trained and potentially overfitted audio representation (embeddings). Here is a short list of examples : — Attention-Based Audio Embeddings [3] — Autoencoder [4] — Contrastive learning [5, 6] 1 Skills Who are we looking for ? Preparing an engineering degree or a master’s degree, you preferably have knowledge in the development and implementation of advanced machine learning algorithms. Digital audio signal processing skills is a plus. Whereas not mandatory, notions in the following additional various fields would be appreciated : Audio effects in general : compression, equalization, etc. - Statistics, probabilist approaches, optimization. - Programming language : Python, Pytorch, Keras, Tensorflow, Matlab. - Voice recognition, voice command. - Computer programming and development : Max/MSP, C/C++/C#. - Audio editing software : Audacity, Adobe Audition, etc. - Scientific publications and patent applications. - Fluent in English and French. - Demonstrate intellectual curiosity. Références [1] A. Wang. « The Shazam music recognition service ». In : Communications of the ACM 49.8 (2006), p. 44-48. [2] A. Wang et al. « An industrial strength audio search algorithm. » In : Ismir. T. 2003. Washington, DC. 2003, p. 7-13. [3] A. Singh, K. Demuynck et V. Arora. « Attention-Based Audio Embeddings for Query-by-Example ». In : arXiv preprint arXiv :2210.08624 (2022). [4] A. Báez-Suárez et al. « SAMAF : Sequence-to-sequence Autoencoder Model for Audio Fingerprinting ». In : ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16.2 (2020), p. 1-23. [5] X. Wu et H. Wang. « Asymmetric Contrastive Learning for Audio Fingerprinting ». In : IEEE Signal Processing Letters 29 (2022), p. 1873-1877. [6] Z. Yu et al. « Contrastive unsupervised learning for audio fingerprinting ». In : arXiv preprint arXiv :2010.13540 (2020).
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