L’équipe SAMoVA de l’IRIT à Toulouse propose plusieurs stages en 2022 :

- Analyse des signaux IoT (audio et accéléromètre) issus du collier Sallis Médical en vue de la modélisation de l’efficacité pharyngolaryngée
- Discriminative sequence training in end-to-end automatic speech recognition
- Le traitement automatique de la parole et l’IA au service du diagnostic différencié de maladies neurodégénératives
- Self- and semi-supervised adaptation of neural speaker diarization
- Self-supervised audio representation learning
- ...

Tous les détails (sujets, contacts) sont disponibles dans la section 'Jobs' de l’équipe, onglet 'Internships' :
https://www.irit.fr/SAMOVA/site/jobs/

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====== Offre de stage en reconnaissance automatique de la parole ========

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The SteelSeries France R&D team (former Nahimic R&D team) is glad to open 4 research internship positions for 2022.

Internship position at Telecom-Paris on Deep learning approaches for social computing

           

*Place of work* Telecom Paris, Palaiseau (Paris outskirt)

 

*Starting date* From February 2021(but can start later)

 

*Duration* 4-6 months

 

*Context*

The intern will take part in the REVITALISE project, funded by ANR.

The research activity of the internship will bring together the research topics of Prof. Chloé Clavel [Clavel] of the S2a [SSA] team at Telecom-Paris– social computing [SocComp] - and Dr. Mathieu Chollet [Chollet] from University of Glasgow – multimodal systems for social skills training, and Dr Beatrice Biancardi [Biancardi] – Social Behaviour Modelling from CESI Engineering School, Nanterre.

 

* Candidate profile*

As a minimum requirement, the successful candidate should have:

• A master degree in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, affective computing, reinforcement learning, natural language processing, speech processing

• Excellent programming skills (preferably in Python)

• Excellent command of English

• The desire to do an academic thesis at Telecom-Paris after the internship

 

*How to apply*

The application should be formatted as **a single pdf file** and should include:

• A complete and detailed curriculum vitae

• A cover letter

• The contact of two referees

The pdf file should be sent to the two supervisors: Chloé Clavel, Beatrice Biancardi and Mathieu Chollet: chloe.clavel@telecom-paris.fr, bbiancardi@cesi.fr, mathieu.chollet@glasgow.ac.uk

 

           

Multimodal attention models for assessing and providing feedback on users’ public speaking ability

 

*Keywords* human-machine interaction, attention models, recurrent neural networks, Social Computing, natural language processing, speech processing, non-verbal behavior processing, multimodality, soft skills, public speaking

 

*Supervision* Chloé Clavel, Mathieu Chollet, Beatrice Biancardi

 

*Description* Oral communication skills are essential in many situations and have been identified as core skills of the 21st century. Technological innovations have enabled social skills training applications which hold great training potential: speakers’ behaviors can be automatically measured, and machine learning models can be trained to predict public speaking performance from these measurements and subsequently generate personalized feedback to the trainees.

The REVITALISE project proposes to study explainable machine learning models for the automatic assessment of public speaking and for automatic feedback production to public speaking trainees. In particular, the recruited intern will address the following points:

-   identify relevant datasets for training public speaking and prepare them for model training

-   propose and implement multimodal machine learning models for public speaking assessment and compare them to existing approaches in terms of predictive performance.

-   integrate the public assessment models to produce feedback a public speaking training interface, and evaluate the usefulness and acceptability of the produced feedback in a user study

The results of the project will help to advance the state of the art in social signal processing, and will further our understanding of the performance/explainability trade-off of these models.

 

The compared models will include traditional machine learning models proposed in previous work [Wortwein] and sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou], [BenYoussef]. The feedback production interface will extend a system developed in previous work [Chollet21].

 

Selected references of the team:

[Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews.  in AAAI 2019, to appear

[Ben-Youssef]  Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim.  Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions.  In  Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464–472. ACM, 2017.

[Wortwein] Torsten Wörtwein, Mathieu Chollet, Boris Schauerte, Louis-Philippe Morency, Rainer Stiefelhagen, and Stefan Scherer. 2015. Multimodal Public Speaking Performance Assessment. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI '15). Association for Computing Machinery, New York, NY, USA, 43–50.

[Chollet21] Chollet, M., Marsella, S., & Scherer, S. (2021). Training public speaking with virtual social interactions: effectiveness of real-time feedback and delayed feedback. Journal on Multimodal User Interfaces, 1-13.

 

Other references:

[TPT] https://www.telecom-paristech.fr/eng/ 

[IMTA] https://www.imt-atlantique.fr/fr

[SocComp.] https://www.tsi.telecom-paristech.fr/recherche/themes-de-recherche/analyse-automatique-des-donnees-sociales-social-computing/

[SSA] http://www.tsi.telecom-paristech.fr/ssa/#

[PACCE] https://www.ls2n.fr/equipe/pacce/

[Clavel] https://clavel.wp.imt.fr/publications/

[Chollet] https://matchollet.github.io/

[Biancardi] https://sites.google.com/view/beatricebiancardi

-Rasipuram, Sowmya, and Dinesh Babu Jayagopi. 'Automatic multimodal assessment of soft skills in social interactions: a review.' Multimedia Tools and Applications (2020): 1-24.

-Sharma, Rahul, Tanaya Guha, and Gaurav Sharma. 'Multichannel attention network for analyzing visual behavior in public speaking.' 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018.

-Acharyya, R., Das, S., Chattoraj, A., & Tanveer, M. I. (2020, April). FairyTED: A Fair Rating Predictor for TED Talk Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 338-345).

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Hi,

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Afin de renforcer son équipe, le laboratoire Commun ALAIA, destiné à l'Apprentissage des langues Assisté par Intelligence Artificielle, propose deux postes d'ingénieurs de recherche (12 mois).

ALAIA est centré sur l'expression et la compréhension orale d'une langue étrangère cible (L2). En collaboration avec ses deux partenaires, académique (IRIT) et industriel (Archean Technologie) ainsi que des experts en didactiques de langues, les missions consisteront à concevoir, développer et intégrer des services innovants basés sur l'analyse des productions des apprenants L2, la détection et la caractérisation d'erreurs allant du niveau phonétique au niveau linguistique. Elles seront affinées en fonction du profil des personnes recrutées.

Les compétences attendues portent sur le traitement automatique de la parole et du langage ainsi que les méthodes de machine learning. 

• Détail de l'offre :  https://www.irit.fr/SAMOVA/site/wp-content/uploads/2021/12/ALAIA_Ingenieur_de_Recherche_2022_Franc%CC%A7ais.pdf
  
• Lien vers le LabCom : https://anr.fr/Projet-ANR-18-LCV3-0001 et https://www.irit.fr/SAMOVA/site/projects/current/labcom-alaia/
• Salaire : selon la grille en vigueur, le profil et l'expérience
• Date limite de candidature : les deux postes sont à pourvoir au plus tôt,
  
• Prise de poste : début février 2022 si possible
  

Les candidatures sont à adresser à Isabelle Ferrané (isabelle.ferrane@irit.fr) et Lionel Fontan (lfontan@archean.tech). N'hésitez pas à nous contacter pour de plus amples informations. 

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1) Research Associate in Safe Conversational AI(re-advertising)

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Aalto opens a call for an assistant or associate professor in speech and language technology. 

https://aalto.wd3.myworkdayjobs.com/PrivateJobPosting/job/Otaniemi-Espoo-Finland/ASSISTANT-OR-ASSOCIATE-PROFESSOR-IN-SPEECH-AND-LANGUAGE-TECHNOLOGY--tenure-track-_R32437

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We are looking to recruit two Post-Doctoral Researchers to join the UCD team in the ADAPT Research Centre (www.adaptcentre.ie).  These projects are part of the ADAPT Digital Content Transformation Strand.

 

1) Harnessing Speech for Social Inclusion

Rather than focusing on accuracy and error rates, evaluation of speech recognition systems should be contextualized with respect to how well they perform in situations where the interlocutor is not the ‘typical’ native speaker (e.g. a senior citizen, a citizen with disabilities or a non-native speaker). Through publicly engaged research, data will be collected on how well existing speech technology is serving our citizens. A systematic evaluation of the output data produced by existing ASR systems and the interactions that arise when an ASR ‘converses’ with a range of users will enable a categorization of interaction issues and error patterns that need to be accounted for when developing applications which provide interfaces to essential services. This project will involve the development of a system which facilitates diagnostic evaluation of ASRs in a variety of interaction scenarios providing linguistic cues the augmentation of the ASR and building on the low-resource MT technologies being developed in other ADAPT projects.

 

2) Embedding of Multi-level Speech Representations

While deep learning has led to huge performance gains in speech recognition and synthesis, only recently more focus is being placed on what deep learning may be able uncover about the patterns which humans use intuitively when interacting via speech and which distinguish native from non-native speakers. Such patterns are typically the focus of speech perception and experimental phonetic studies. This project aims to build on the notion of multi-linear or multi-tiered representations of speech, creating embeddings of multiple (sub- word) levels of representation – phonetic features, phonemes, syllable pieces and syllables – enabling a closer investigation of systematicity and variability of speech patterns. This research will find application in non-native speech recognition, in speech adaptation/accommodation for native and non-native interactions and in pronunciation training scenarios.

 

ADAPT is the world-leading SFI research centre for AI Driven Digital Content Technology, coordinated by Trinity College Dublin and based within Dublin City University, University College Dublin, Technological University Dublin, Maynooth University, Munster Technological University, Athlone Institute of Technology, and the National University of Ireland Galway. ADAPT’s research vision is to pioneer new forms of proactive, scalable, and integrated AI-driven Digital Content Technology that empower individuals and society to engage in digital experiences with control, inclusion, and accountability with the long-term goal of a balanced digital society by 2030. ADAPT is pioneering new Human Centric AI techniques and technologies including personalisation, natural language processing, data analytics, intelligent machine translation human-computer interaction, as well as setting the standards for data governance, privacy and ethics for digital content.

ADAPT Digital Content Transformation Strand
From the algorithmic perspective, new machine learning techniques will both enable more users to engage meaningfully with the increasing volumes of content globally in a more measurably effective manner, while ensuring the widest linguistic and cultural inclusion. It will enhance effective, robust integrated machine learning algorithms needed to provide multimodal content experiences with new levels of accuracy, multilingualism and explainability.

 

Full details of the positions, requirements and link to submit applications can be found at:

 

1) https://www.adaptcentre.ie/careers/postdoctoral-researcher-in-harnessing-speech-for-social-inclusion/

2) https://www.adaptcentre.ie/careers/postdoctoral-researcher-in-embedding-multi-level-speech-representations-2/

 

The closing date is 17:00hrs (local Irish time) on Monday 14th February 2022 and and candidates must apply via https://www.ucd.ie/workatucd/jobs/  

 

The reference is 

 

1) 014028 for Harnessing Speech for Social Inclusion

2) 014079 for Embedding of Multi-level Speech Representations

 

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A public selection procedure is called, based on qualifications and interview, for the assignment of n. 2 research fellowship grant for collaboration in research activities

Project main aim: Multidisciplinary Research on AI for Health.

Location: Kore University of Enna - Enna (Sicily), Italy

Funding Programme: Research Projects of National Relevance - PRIN 2017

Description: The project focuses on idiopathic Parkinson’s disease dysarthric speech, produced by speakers of two varieties of Italian that show different segmental (consonantal, vocalic) and prosodic characteristics. The project as a whole aims at: identifying phonetic features that impact on speech intelligibility and accuracy, separating variability due to dysarthria from features due to sociolinguistic variation, and developing perspectives and tools for clinical practice that take variation into account.

Duration: 12 months

Link to apply: https://unikore.it/index.php/it/contratti-di-ricerca/item/41282-d-p-n-33-2022-2-assegni-di-ricerca-presso-l-universita-degli-studi-di-enna-kore

Further information contact Prof. Sabato Marco Siniscalchi, E-mail: marco.siniscalchi-at-unikore.it

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Job description

One of the most pressing matters that holds back robots from taking on more tasks and reach a widespread deployment in society is their limited ability to understand human communication and take situation-appropriate actions. This PhD positions is dedicated to addressing this gap by developing the underlying data-driven models that enable a robot to engage with humans in a socially aware manner.

This position is specifically targeted at the development of an argumentative dialogue system for human-robot interaction.  The PhD candidate will explore how to fuse multimodal behaviour to infer a person's perspective. The candidate will use, and further develop, reinforcement learning techniques in order to drive the robot's argumentative strategy for deliberating topics of current social importance such as global warming or vaccination.

The ideal candidate will have a keen interest in speech technology and reinforcement learning. He or she has strong interactive system background will design and run the experiments to evaluate the created hybrid-AI models through human-robot interaction.

Topics of interest:

1) long-term human-robot interaction

2) affective computing

2) NLP&argument-mining

Requirements

• MSc in Computer Science or related field.
• At least 3 years of programming experience in python (java  or C++is a plus).
• Motivation to meet deadlines.
• Affinitty to design and social science research.
• Interest in collaborating with colleagues from Industrial Design.
• Willingness to teach and guide students.
• The ability to work in a team, take initiative, be results oriented and systematic.

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Postdoctoral position in Speech Processing at INRIA, Bordeaux, France

Title: Glottal source inverse filtering for the analysis and classification of pathological speech

Keywords: Pathological speech processing, Glottal source estimation, Inverse filtering, Machine learning, Parkinsonian disorders, Respiratory diseases

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

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

Duration: 13 months (could be extended)

Starting date: between 01/04/2022 and 01/06/2022 (depending on the candidate availability)

Application : via https://recrutement.inria.fr/public/classic/en/offres/2022-04481

Salary: 2653€/month (before taxes, net salary 2132€)

Profile: PhD thesis in signal/speech processing (or a solid post-thesis experience in the field) Required Knowledge and background: A solid knowledge in speech/signal processing; Basics of machine learning; Programming in Matlab and Python. Scientific research context During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages [2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic. The speech production mechanism is essentially governed by five subsystems: respiratory, phonatory, articulatory, nasalic and prosodic. In the framework of pathological speech, the phonatory subsystem is the most studied one, usually using sustained phonation (prolonged vowels). Phonatory measurements are generally based on perturbations or/and cepstral features. Though these features are widely used and accepted, they are limited by the fact that the produced speech can be a product of some or all the other subsystems. The latter thus all contribute to the phonatory performance. An appealing way to bi-pass this problem is to try to extract the glottal source from speech in order to isolate the phonatory contribution. This framework is known as glottal source inverse filtering (GSIF) [3]. The primary objective of this proposal is to investigate GSIF methods in pathological speech impaired by dysarthria and respiratory deficit. The second objective is to use the resulting glottal parameterizations as inputs to basic machine learning algorithms in order to assist in the discrimination between subgroups of Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in the monitoring of respiratory diseases (Covid-19, Asthma, COPD). Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian disorders) and in Paris (for respiratory diseases).

Work description GSIF consists in building a model to filter out the effect of the vocal tract and lips radiation from the recorded speech signal. This difficult problem, even in the case of healthy speech, becomes more challenging in the case of pathological speech. We will first investigate time-domain methods for the parameterization of the glottal excitation using glottal opening and closure instants. This implies the development of a robust technique to estimate these critical time-instants from dysarthric speech. We will then explore the alternative approach of learning a parametric model of the entire glottal flow. Finally, we will investigate frequency-domain methods to determine relationships between different spectral measures and the glottal source. These algorithmic developments will be evaluated and validated using a rich set of biosignals obtained from patients with Parkinsonian disorders and from healthy controls. The biosignals are electroglottography and aerodynamic measurements of oral and nasal airflow as well as intra-oral and sub-glottic pressure. After dysarthric speech GIFS analysis, we will study the adaptation/generalization to speech impaired by respiratory deficits. The developments will be evaluated using manual annotations, by an expert phonetician, of speech signals obtained from patients with respiratory deficit and from healthy controls. The second aspect of the work consists in manipulating machine learning algorithms (LDA, logistic regression, decision trees, SVM…) using standard tools (such as Scikit-Learn). The goal here will be to study the discriminative power of the resulting speech features/measures and their complementarity with other features related to different speech subsystems. The ultimate goal being to conceive robust algorithms to assist, first, in the discrimination between Parkinsonian disorders and, second, in the monitoring of respiratory deficit.

Work synergy - The postdoc will interact closely with an engineer who is developing an open-source software architecture dedicated to pathological speech processing. The validated algorithms will be implemented in this architecture by the engineer, under the co-supervision of the postdoc. - Giving the multidisciplinary nature of the proposal, the postdoc will interact with the clinicians participating in the two clinical studies.

References: [1] J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013. [2] J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015. [3] P. Alku. Glottal inverse filtering analysis of human voice production – A review of estimation and parameterization methods of the glottal excitation and their applications. Sadhana – Academy Proceedings in Engineering Sciences. Vol. 36, Part 5, pp. 623-650, 2011

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La jeune compagnie EasyChain et le laboratoire de recherche L3i de La Rochelle lancent un appel à candidatures pour un poste de doctorant.e CIFRE dans le domaine de développement des agents conversationnels.

Les détails de l'offre sont disponibles à cette adresse :
Dynamic Human-Agent Interactions adapted to users? profiles.

Le.la candidat.e retenu.e devra être titulaire d'un master ou d'un diplôme équivalent en informatique ou en traitement automatique des langues. Un solide bagage en apprentissage automatique et une bonne communication en anglais sont requis.

La thèse se déroulera principalement à Niort chez EasyChain, dans un environnement francophone.

Si vous êtes intéressé.e par ce poste, veuillez envoyer les informations suivantes à Antoine Doucet (antoine.doucet@univ-lr.fr),  et Ahmed Hamdi (ahmed.hamdi@univ-lr.fr):
 * CV détaillé
 * Diplômes de licence et de master.
* Lettres de recommandation

Les candidatures seront étudiées jusqu'au 1 mars 2022.

Bien cordialement

Antoine Doucet

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• Le doctorat ou le postdoctorat sera mené au sein de l'équipe Getalp du laboratoire LIG  (https://lig-getalp.imag.fr/).
• La personne recrutée sera accueillie au sein de l?équipe qui offre un cadre de travail stimulant, multinational  et agréable. 
• Les moyens pour mener à bien le (post)doctorat seront assurés tant en ce qui concerne les missions en France et à l?étranger qu?en ce qui concerne le matériel (ordinateur personnel, accès aux serveurs GPU du LIG, Grille de calcul Jean Zay du CNRS).

• Pour postuler sur une thèse de doctorat, les candidats doivent être titulaires d'un Master en informatique, en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants actuellement en master 2 peuvent ainsi postuler).
• Pour postuler sur un postdoctorat, les candidats doivent être titulaires d'une thèse de doctorat en informatique,  en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants dont la soutenance est prévue avant fin septembre 2022 peuvent ainsi postuler).
• Ils doivent avoir une bonne connaissance des méthodes d?apprentissage automatique et idéalement une expérience en collecte et gestion de corpus.
• Ils doivent également avoir une bonne connaissance de la langue française. 

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2022-04676 - PhD Position F/M Nongaussian models for deep learning based audio signal processing Level of qualifications required :Graduate degree or equivalent Fonction : PhD Position Context The PhD student will join the Multispeech team of Inria,that is the largest French research group in the field of speech processing. He/she will benefit from the research environment and the expertise in audio signal processing and machine learning of the team, which includes many researchers, PhD students, post-docs, and software engineers working in this field. He/she will be supervised by Emmanuel Vincent (Senior Researcher, Inria), and Paul Magron (Researcher, Inria). Assignment Audio signal processing and machine listeningsystems have achieved considerable progress over the past years, notably thanks to the advent of deep learning. Such systems usually process a timefrequency representation of the data, such as a magnitude spectrogram, and model its structure using a deep neural network (DNN). Generally speaking, these systems implicitly rely on the local Gaussian model [1],that is an elementary statistical model for the data. Even though it is convenient to manipulate, this model builds upon several hypotheses which are limiting in practice: (i) circular symmetry, which boils down t o discarding the phase information (= the argument of the complex-valued time-frequency coefficients); (ii) independence of the coefficients, which ignores the inherent structure of audio signals (temporal dynamics, frequency dependencies); and (iii)Gaussian density, which is not observed in practice. Statistical audio signal modeling is an active research field. However, recent advances in this field are usually not leveraged in deep learning-based approaches, thus their potential is currently underexploited. Besides, some of these advances are not mature enough to be fully deployed yet. Therefore, the objective of this PhD is to design advanced statistical signal models for audio which overcome the limitations of the local Gaussian model, while combining them with DNN-based spectrogram modeling. The developed approaches will be applied to audio source separation and speech enhancement. Main activities The main objectives of the PhD student will be: 1. To develop structured statistical models for audio signals, which alleviate the limitations of the local Gaussian model. In particular, t he PhD student will focus on designing models by leveraging properties that originate from signal analysis, such as the temporal continuity [2] or the consistency of the representation [3], in order to favor interpretability and meaningfulness of the models. For instance, alpha-stable distributions have been exploited in audio for their robustness [4]. Anisotropic models are an interesting research direction since they overcome the circular symmetry assumption, while enabling an interpretable parametrization of the statistical moments [5]. Finally, a careful design of the covariance matrix allows for explicitly incorporating time and frequency dependencies [6]. 2. To combine these statistical models withDNNs. This raises several technical difficulties regarding the design of, e.g., the neural architecture, the loss function, and the inference algorithm. The student will exploit and adapt the formalism developed in Bayesian deep learning, notably the variational autoencoding framework [7], as well as the inference procedures developed in DNN-free nongaussian models [8]. 3. To validate experimentally these methods on realistic sound datasets. To that end, the PhD student will use public datasets such as LibriMix (speech) and MUSDB (music), which are reference datasets for source separation and speech enhancement. The PhD student will disseminate his/her research results in international peer-reviewed journals and conferences. In order to promote reproducible research, these publications will be self-archived at each step of the publication lifecycle, and accessible through open access repositories (e.g., arXiv, HAL). The code will be integrated to Asteroid, that is the reference soDware for source separation and speech enhancement developed by Multispeech. Bibliography [1] E. Vincent, M. Jafari, S. Abdallah, M. Plumbley, M. Davies,Probabilistic modeling paradigms for audio source separation, Machine Audition: Principles, Algorithms and Systems, p.162–185, 2010. [2] T. Virtanen, Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 15, no. 3, pp.1066-1074, 2007. [3] J. Le Roux, N. Ono, S. Sagayama, Explicit consistency constraints for STFT spectrograms and their application to phase reconstruction, Proc. SAPA, 2008. [4] S. Leglaive, U. Şimşekli, A. Liutkus, R. Badeau and G. Richard,Alpha-stable multichannel audio source separation, Proc. IEEE ICASSP, 2017. [5] P. Magron, R. Badeau, B. David, Phase-dependent anisotropic Gaussian model for audio source separation, Proc. IEEE ICASSP, 2017. [6] M. Pariente, Implicit and explicit phase modeling in deep learning-based source separation, PhD thesis - Université de Lorraine, 2021. [7] L. Girin, S. Leglaive, X. Bie,J. Diard, T. Hueber, X. Alameda-Pineda,Dynamical variational autoencoders: A comprehensive review, Foundations and Trends in Machine Learning, vol. 15, no. 1-2, 2021. General Information Theme/Domain : Language, Speech and Audio Town/city : Villers lès Nancy Inria Center : CRI Nancy - Grand Est Starting date : 2022-10-01 Duration of contract : 3 years Deadline to apply : 2022-05-02 Contacts Inria Team : MULTISPEECH PhD Supervisor : Magron Paul / paul.magron@inria.fr About Inria Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, oDen at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. The keys to success Upload your complete application data. Applications will be assessed on a rolling basis, thus it is advised to apply as soon as possible. Instruction to apply Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. [8] P. Magron, T. Virtanen, Complex ISNMF: a phase-aware model for monaural audio source separation, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 27, no. 1, pp. 20-31, 2019. Skills Master or engineering degree in computer science, data science, signal processing, or machine learning. Professional capacity in English (spoken, read, and written). Some programming experience in Python andin somedeep learning framework (e.g., PyTorch). Previous experience and/or interest for speech and audio processing is a plus. Benefits package Restauration subventionnée Transports publics remboursés partiellement Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement) Possibilité de télétravail (après 6 mois d'ancienneté) et aménagement du temps de travail Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.) Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria) Accès à la formation professionnelle Sécurité sociale Remuneration Salary: 1982€ gross/month for 1st and 2 year. 2085€ gross/month for 3rd year. Monthly salary after taxes : around 1594€ for 1st and 2 year. 1677€ for 3rd year

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Contexte
ELDA (Evaluation and Language resources Distribution Agency, www.elda.org) a pour activités principales la distribution et la production de ressources linguistiques, ainsi que l’évaluation de technologies de la langue (traduction automatique, reconnaissance de la parole, etc.).

Dans le cadre de ses activités de production, ELDA offre plusieurs postes de traducteur peul-français (H/F) à temps plein ou partiel pour la constitution d’un corpus de traduction de la langue.

Mission
Il s’agira de traduire à partir des documents audios et de leurs transcriptions, du peul vers le français afin de fournir les données nécessaires au développement et à l'évaluation de technologies de la langue peul. Le travail sera effectué selon des conventions de traduction sur lesquelles les candidats seront formés.

Profil recherché
• Natif de la langue peul, plus précisément de Mâssina
• Une première expérience en traduction du peul vers le français est souhaitable
• Excellente maîtrise du français écrit (orthographe, grammaire, syntaxe) pour la traduction et la compréhension des conventions de traduction
• Bonne maîtrise de l’outil informatique
• Capacité à intégrer des règles (de traduction) et à les suivre scrupuleusement et avec constance
 
Durée
Temps plein ou mi-temps, pour une durée de 5 mois minimum

Salaire
Salaire mensuel brut: 1604€

Les candidatures (CV, lettre de motivation) doivent être adressées à lucille@elda.org

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Le Collegium Musicæ de Sorbonne Université propose un contrat doctoral sur le style vocal :

 Analyse par la Synthèse performative du style vocal - Porteurs de projet : Christophe d'Alessandro  et Céline  Chabot-Canet

Le propos de cette thèse est l’étude du style vocal par le paradigme d’analyse par la synthèse
performative. Ce sujet associe recherche musicologique sur le style vocal et la musicologie de la
performance, recherche musicale sur les instruments chanteurs, et recherche en informatique musicale
sur les nouvelles interfaces pour l’expression musicale et les synthétiseurs vocaux temps-réel.

des détails se trouvent ici (aller sur l'onglet Collegium Musicæ de la page) :

https://www.sorbonne-universite.fr/projets-proposes-en-2022-programme-instituts-et-initiatives

Contact:

Christophe d'Alessandro : christophe.dalessandro@sorbonne-universite.fr

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