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ISCApad #301 |
| Thursday, July 06, 2023 by Chris Wellekens |
6 Jobs
| 6-1 | (2023-01-02) POSTDOC 21 MONTHS at GIPSA-Lab, Grenoble-France POSTDOC 21 MONTHS at GIPSA-Lab, Grenoble-France
on the automatic evaluation of computer-assisted reading fluency of young French readers' Contact: Gerard Bailly at gerard.bailly@gipsa-lab.fr
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| 6-2 | (2023-01-03) Stage de recherche projet CAIBots: Conversational AI with teams of robots, LIA, Avignon, France FORMULAIRE DE STAGE RECHERCHE Intitulé du projet CAIBots: Conversational AI with teams of robots Encadrants Prof. Fabrice Lefèvre Descriptif du stage : L'objectif du stage consiste à étudier la mise en place d’un dispositif robotique permettant la simulation en « conditions réelles » des IA conversationnelles (CAI) vocales. Entraîner puis tester de l’IA conversationnelle (chatbots, systèmes de dialogue) est couteux et complexe, nous souhaitons grandement réduire cette difficulté en fournissant une solution robotique physique autonome pour apprendre et évaluer de nouveaux modules pour la CAI avant de les utiliser avec de vrais utilisateurs humains. Dans un premier temps, il s’agira principalement de tester des solutions existantes et clefs en main pour les éléments de la chaîne de traitement du langage parlé et de vérifier leur niveau de performance en configuration robot-robot. Ensuite une recherche vers des solutions embarquées sera menée. Elle devra permettre d’améliorer la latence du dispositif mais aussi d’assurer une meilleure protection des données personnelles (en ôtant la nécessité du passage par des clouds propriétaires). Globalement le système d'interaction vocal mis en place devra permettant une discussion ouverte entre un humain et une machine sur des sujets généraux. Le cas d’usage envisagé se positionne donc dans la logique du challenge Amazon Alexa (https://developer.amazon.com/alexaprize) : développer un bot pouvant entretenir une conversation pendant quelques minutes. Il sera donc nécessaire de prévoir aussi un utilisateur simulé pour permettre une interaction robot-robot autonome (le cas de conversations multiparties humains-robots pourra aussi être testé, sans être un objectif prioritaire du stage). Il s'agira d’initier le dispositif, c'est à dire de mettre en place les composants en configuration de base, mais illustrant les capacités potentielles pouvant être atteintes avec un temps de développement plus conséquent. Les solutions robotiques et logicielles entrevues pour ce travail sont, par exemple : robot Pepper, Google Cloud ASR, SpeechBrain, RASA et/ou des modèles pré-entraînés (BERT, GPT, BlenderBot…) ... Il s’agit principalement de plateformes open-source, assez complètes. Le travail consistera à mettre en œuvre rapidement un système réel afin de pouvoir le faire progresser en configuration robot-robot puis le tester avec un panel représentatif d'utilisateurs potentiels. Si un intérêt pour l'apprentissage automatique et le traitement de la langue naturelle est essentiel, il est aussi attendu du stagiaire de bonnes capacités en développement logiciel. Le stage sera une occasion d'acquérir des compétences en traitement automatique de la langue dans un contexte d'expérimentation en robotique embarquée. Plusieurs pistes pour une prolongation en thèse sont ouvertes. Durée du stage 6 mois Rémunération Environ 540€ / mois Thématique associée au stage Systèmes de dialogue humain-machine, reconnaissance et compréhension de parole, interface cognitive, robotique
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| 6-3 | (2023-01-05) Post-doctoral and engineer 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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| 6-4 | (2023-01-15) Stage M2, LIA, Avignon, France Sujet de stage de M2 : Décodage des signaux EEG à l’aide des méthodes d’apprentissage automatique Contexte L’EEG (électroencéphalographie) est une technique non invasive, qui permet de mesurer l’activité électrique du cerveau à l’aide d’électrodes placées sur la tête. Ces électrodes enregistrent l’activité électrique causées par les neurones. Les données recueillies sont enregistrées et peuvent être, à l’aide des méthodes d’apprentissage automatique, utilisées à diverses fins comme par exemple : analyse, classification ou interface neuronale directe [Cao20]. Dans le cadre du traitement automatique du langage et de la parole, des premiers travaux, avec des résultats préliminaires, sont apparus récemment (e.g. classification EEG avec une approche par Transformer [Sun21], des données EEG issues de la lecture de phrases [Hollenstein18] ou encore dans le cadre de méthodes combinant l’utilisation d’EEG avec des techniques de traitement du langage pour la détection de préférences utilisateur [Gauba17]). Objectif L’objectif de ce stage consiste à reconnaître automatiquement des caractères dans un premier temps, puis des mots isolés, énoncés oralement via les signaux EEG. Les différentes étapes du stage peuvent se résumer comme suit : 1. Prise en main du casque EEG Emotiv EPOC-X (https://www.emotiv.com/epoc-x/). 2. Mettre en place un protocole expérimental et collecter un corpus permettant la mise en place des expériences. 3. Evaluer et choisir des algorithmes d’apprentissage automatique pour reconnaitre les caractères et/ou mots isolés à partir des signaux EEG du corpus collecté dans le cadre du stage. Profil du candidat L’étudiant.e doit être en dernière année de diplôme d’ingénieur ou en Master 2 d’informatique. Il ou elle doit posséder des notions de programmation, maîtriser l’environnement Linux et les méthodes standard d’apprentissage automatique. Durée du stage 6 mois de février à mars 2023. Lieu du stage Le stage aura lieu au sein du Laboratoire Informatique d’Avignon (LIA) à Avignon Université ou au sein du Laboratoire des Sciences du Numérique de Nantes (LS2N) à l’Université de Nantes. Gratification Le stage sera gratifié selon le montant horaire en vigueur au 01/01/2023, considérant une convention de stage de 35 heures par semaine (4,05 euros soit ≃550 €/mois). Comment postuler ? Merci d’envoyer par email à Mickael Rouvier (mickael.rouvier@univavignon.fr) et Richard Dufour (richard.dufour@univ-nantes.fr) les documents suivants : 1) CV ; 2) relevé de notes (licence et master) et 3) lettre de motivation. Bibliographie [Cao20] Cao, Z. (2020). A review of artificial intelligence for EEG-based brain− computer interfaces and applications. Brain Science Advances, 6(3), 162-170. [Gauba17] Gauba, H., Kumar, P., Roy, P. P., Singh, P., Dogra, D. P., & Raman, B. (2017). Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks, 92, 77-88. [Hollenstein18] Hollenstein, N., Rotsztejn, J., Troendle, M., Pedroni, A., Zhang, C., & Langer, N. (2018). ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Scientific data, 5(1), 1-13. [Sun21] Sun, J., Xie, J., & Zhou, H. (2021). EEG classification with transformer-based models. In 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech) (pp. 92-93).IEEE
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| 6-5 | (2023-01-15) Ref 6581/22, Postdoctoral Research Fellow, MARCS Institute for Brain, Behaviour & Development ,Canberra, Australia Ref 6581/22, Postdoctoral Research Fellow, MARCS Institute for Brain, Behaviour & Development About Western Western Sydney University is a modern, forward-thinking, research-led university, located at the heart of Australia’s fastest-growing and economically significant region, Western Sydney. Boasting 11 campuses – many in Western Sydney CBD locations – and more than 200,000 alumni, 49,500 students and 3,500 staff, the University has 14 Schools with an array of well-designed programs and degrees carefully structured to meet the demands of future industry. The University is ranked in the top two per cent of universities worldwide, and as a research leader, over 85 per cent of the University’s assessed research is rated at ‘World Standard’ or above. About the Role The MARCS Institute for Brain, Behaviour and Development is seeking to appoint a Postdoctoral Research Fellow to join the Brain Sciences research program at Western Sydney University. This two year Postdoctoral Research Fellow position is funded by an ARC Discovery grant, “Investigating the characteristics of older adults' conversation behaviour” awarded to Chief Investigators (CIs) Professor Chris Davis and Professor Jeesun Kim at the MARCS Institute for Brain, Behaviour and Development, Western Sydney University, in collaboration with Partner Investigators (PI) Emeritus Professor Valerie Hazan at University College London. This project will investigate the production and perception of naturalistic conversations by young and older adults. In particular, the interest is in probing individualised semantic processing. The aim of the project is to understand factors that affect the engagement of older adults in conversations. In this role you will work with the team to develop, pilot, and apply new methods for probing individual-specific semantic processing and on the collection of sensory, perceptual, cognitive and electrophysiological (EEG) data. You will also implement procedures for analysing the data. The successful applicant will work closely with the above investigators and the rest of the research team, conducting studies and participating in supervising and/or training PhD students, Research Assistants, Honours students, and interns enlisted into the project. For further information about the role please refer to the attached Position Description. This is a full time, 2 year fixed term contract. Located in the new Westmead Innovation Quarter (WIQ) – a visionary research, health, education and business hub located in the Westmead Health Precinct. About MARCS The MARCS Institute for Brain, Behaviour and Development is an interdisciplinary research institute of Western Sydney. The vision for the Institute is to optimise human interaction and wellbeing across the lifespan. To strive to solve the problems that matter most through the themes: sensing and perceiving, interacting with each other, and technologies for humans. Researchers in MARCS come from many disciplines including cognitive science, developmental psychology, language science, music science, cognitive neuroscience, and biomedical, electrical, electronic and software engineering. Further information is available from our website - http://www.westernsydney.edu.au/marcs About You You will hold a relevant doctoral qualification, or substantial progress toward a PhD in Psychology, Cognitive Neuroscience, Computational linguistics or a related discipline. You must have experience in conducting neuroimaging and behavioural experiments. Culture Western Sydney University highly values equity and inclusiveness. We have a proud history of doing so and consider this an important part of our social and civic responsibilities as a University. We strive to contribute to tackling inequalities and promoting wellbeing within our own institution, the Greater Western Sydney region, nationally and internationally. Remuneration Package: Academic Level A: $103,304 to $124,808 p.a. (comprising Salary of $87,293 to $105,464 p.a., plus Superannuation and Leave Loading) Academic Level B: $131,136 to $154,738 p.a. (comprising Salary of $110,811 to $130,846 p.a., plus Superannuation and Leave Loading) Position Enquiries: Please contact Professor Chris Davis via email at chris.davis@westernsydney.edu.au Closing Date: 8:30pm AEDT, Sunday, 12 February 2023 Immigration Sponsorship: Employer Visa sponsorship will be provided if required. Click here to view Position Description How to Apply: · Start your application by clicking the 'begin' button. · Login to an existing account or reset your password · Preview Application Form Western Sydney University is committed to diversity and social inclusion. Applications from people of culturally and linguistically diverse backgrounds; equity target groups including women, people with disabilities, people who identify as LGBTIQ, and people of Aboriginal and Torres Strait Islander descent are encouraged. Professor Chris Davis, PhD The MARCS Institute for Brain, Behaviour and Development Western Sydney University Westmead Innovation Quarter Building U, Level 4 160 Hawkesbury Road (Corner of Farmhouse Road) Westmead NSW 2145 <chris.davis@westernsydney.edu.au>
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| 6-6 | (2023-01-16) Research Fellow Chairs @MIAI,Grenoble Interdisciplinary Institute, France
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| 6-7 | (2023-01-17) Two postdoctoral positions @ University of Cambridge, UK Senior Postdoctoral Position in SLP University of Cambridge, Department of Engineering (UK) The ALTA Institute is looking for a senior postdoctoral researcher in spoken language processing to join our research team investigating L2 English speaking automated assessment and learning. Website: https://www.jobs.cam.ac.uk/job/39114/
Postdoctoral Position in SLP University of Cambridge, Department of Engineering (UK) The ALTA Institute is looking for a postdoctoral researcher in spoken language processing to join our research team investigating L2 English speaking automated assessment and learning. Website: https://www.jobs.cam.ac.uk/job/39023/
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| 6-8 | (2023-01-25) Master 2 internship @ LISN, Orsay, France Creation of a speech synthesis model from spontaneous speech Keywords: Machine learning, speech synthesis, low resource languages, Nigerian Pidgin Objectives The main aim is to produce a natural-sounding text-to-speech (TTS) model allowing to perform perceptual tests for experimental linguistics. Thanks partly to the recent evolution of neural network-based speech technologies, researchers can now produce high-quality synthesis from relatively simple datasets using models like TacoTron 2, complementing classical approaches such as those based on Hidden Markov Models. Specifically, the intern will assist in developing a text-to-speech platform trained on an existing database of Nigerian Pidgin recordings. In addition to producing natural-sounding speech, a central goal of this project will be to build a TTS model that will allow for the direct modification of intonational patterns via explicit parameters provided by researchers. The intern’s work will contribute to the exploration of the language’s melodic and tonal properties by allowing researchers to produce variations of novel utterances differing only by their intonational patterns. Context This work is part of a larger project to study Nigerian Pidgin. It is a large but under-resourced language that increasingly serves as the primary vernacular language of Africa’s most populous country. Once stigmatized as a “broken” variety of English spoken only by the uneducated, Nigerian Pidgin is now a source of pride for many speakers who view it as a home-grown vehicle for communication. It transcends class and ethnicity, lacking the tribal associations of indigenous languages and the colonial baggage associated with English. The language can now be seen and heard in college campuses, houses of worship, advertisements, Nigerian expat communities, and even on a local branch of the BBC. Primary tasks • Surveying existing TTS models and selecting the most suitable approach • Training a model on a corpus of Nigerian Pidgin • Optimizing and evaluating the model Profile
A second-year master’s student with: • A solid background in machine learning (speech synthesis is a plus) • Good academic writing skills in English • An strong interest in language and linguistics
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| 6-9 | (2023-01-26) Poste de maître de conférences en informatique, Nantes, France Nantes Université ouvre un poste de maître de conférences en informatique pour septembre 2023. L'enseignement sera effectué au sein de la Faculté des Langues et Cultures Etrangères (FLCE) et la recherche sera menée au sein du LS2N (Laboratoire des Sciences du numérique de Nantes). Concernant les attendus du poste, la fiche descriptive est consultable à l'adresse : https://uncloud.univ-nantes.fr/index.php/s/ERdm9t8WPNdCn8m
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| 6-10 | (2023-01-30) Vacancy for a university professor in computer science at Bordeaux INP, France Call for applications:
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- Research in computer music in the image and sound department of the LaBRI (www.labri.fr) and at SCRIME (scrime.u-bordeaux.fr) - Teaching at ENSEIRB-MATMECA ( https://enseirb-matmeca.bordeaux-inp.fr/fr) in the computer science department - Schedule: applications between February 23 2023, and March 30 2023, start September 2023 - More information : https://enseirb-matmeca.bordeaux-inp.fr/fr/enseignants
- Contact : myriam.desainte-catherine@labri.fr
Applicants must propose a research project that fits within the image and sound department of the LaBRI to work in particular with the Sound and Music Modeling group, and create links with the Manao team and the Analysis and Indexing group. Candidates must also propose a project for the SCRIME research platform (Studio de Recherche et de Création en Informatique et Musiques Expérimentales) following the departure of the current director. The research area is computer music (sound and music computation, musical interaction). The candidates must be involved in at least one of the following themes:
- computer processing of music and sound: analysis, transformation and generation of music and sound, including environmental sounds and soundtracks of ecological videos, by computational approaches (algorithms, signal processing, learning) in all dimensions of music and sound (timbre, pitch, dynamics and spatialization). - Sound and music interaction: designing new Interfaces between users and computers to create new means of musical expression, through the design of virtual/mixed/augmented sound reality systems, and new models of musical scores and instruments, through interaction with images and other media, and through the use of sound as a means of information. - understanding and modeling of sound and music: music information retrieval, computational musicology, computational approaches to music cognition, formal models and languages for music (time and space of sound and music parameters) - design of new tools for sound and music creation, performance and pedagogy: development of tools to assist sound design and music composition, scenarization, sonification, spatialization (includes algorithmic composition, especially by learning techniques), includes research of software architectures and languages combining micro (sound) and macro (musical form) levels, frugality of computations and transfers of sound and music data, minimization of sound transmission delay, formal specifications for tool preservation.
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| 6-11 | (2023-02-01) Post-doctoral and engineer 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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| 6-12 | (2023-02-01) Master or engineer internship at Loria, Nancy, France Master or engineer internship at Loria (France)
Development of language model for business agreement use cases
Duration: 6 months, starting February or March 2023
Location: Loria (Nancy) and Station F, 5 Parvis Alan Turing, 75013, Paris
Supervision: Tristan Thommen (tristan@koncile.ai), Irina Illina (illina@loria.fr) and Jean-Charles Lamirel (jean-charles.lamirel@loria.fr)
Please apply by sending your CV and a short motivation letter directly to Tristan Thommen and Irina Illina.
Motivations and context
The usage of pre-trained models like Embeddings from Language Models (ELMo) (Peters et al., 2018), Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019), Robustly optimized BERT approach (RoBERTa) (Liu et al., 2019c), Generative Pre-Trained Transformer (GPT) (Radford et al., 2018), etc. proved to be state-of-the-art for various Natural Language Model (NLP) tasks. These models are trained on a huge unlabeled corpus and can be easily fine-tuned to various downstream tasks using task-specific datasets. Fine-tuning involves adding new task-specific layers to the model and updating the pretrained model parameters along with learning new task-specific layers.
Objectives
The goal of the internship is to develop a language model specific to business agreement use cases. This model should be able to identify and extract non-trivial information from a large mass of procurement contracts, in English and in French. This information consists, on the one hand, of simple contract identification data such as signature date, name of the parties, contract title, signatories, and on the other hand, of more complex information to be deduced from clauses, in particular, price determination according to parameters such as date or volume, renewal or expiry, obligations for the parties as well as the conditions. The difficulty of this task is that all this information is not standardized and may be represented in different ways and in different places in an agreement. For instance, a price could be based on a formula defined in the articles of the agreement and an index defined in one of its appendices.
To develop this language model we propose to fine-tune a pre-trained language model using a business agreement use dataset. The intern will identify the relevant pre-trained language model, prepare the data for training and adjust the parameters of fine-tuning.
The particularity of the internship is to use case relevant information of management of business agreements. Datasets will be constituted by Koncile’s clients and partners and developed during this internship models will be directly put into practice and tested with end users.
Koncile (link) is a start-up based in Paris, founded in 2022, that tackles the issue of mismanagement of procurements agreements by companies. It intends to leverage natural
langage processing techniques to analyze supplier contracts and invoicing. Koncile is incubated by Entrepreneur First and hosted at Station F in Paris.
Additional information and requirements
A good practice in Python and basic knowledge about Natural Langage Processing techniques are required. Some notions of machine learning is a plus, both theoretical and practical (e.g., using PyTorch).
References
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227–2237.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In 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), pages 4171–4186.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019c). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding by generative pre-training.
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| 6-13 | (2023-02-06) Associate professor (tenure position) @Telecom Paris in Machine Learning for Socia lComputing 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: - Neural models for the recognition and generation of socio-emotional behaviors - Natural language and speech processing - Dialogue, conversational systems, and social robotics - Reinforcement learning for dialogue - Sentiment analysis in social interactions - Bias and explainability in AI - Model tractability, multi-task learning, meta-learning Salary: between 40,58 k€ and 58,67 k€ depending on profile and experience Important Dates - March 20th, 2023: closing date for applications - April 20th, 2023: hearings of the preselected candidates 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 <https://www.ip-paris.fr/en/> (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 » <https://www.telecom-paris.fr/times-higher-education-telecom-paris-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 - Develop groundbreaking research in the field of machine learning applied to Social Computing, which includes: natural language and speech processing, dialogue, conversational systems, and social robotics, reinforcement learning for dialogue, sentiment analysis in social interactions, bias and explainability in AI, model tractability, multi-task learning, meta-learning - Develop both academic and industrial collaborations on the same topic, including collaborative activities with other Telecom Paris research departments and teams (including social sciences researchers of economics and social sciences department [6]), and research contracts with industrial players - Set up research grants and take part in national and international collaborative research projects - Publish high-quality research work in leading journals and conferences - Be an active member of the research community (serving on scientific committees and boards, organizing seminars, workshops, and special sessions...) 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: - A Ph.D. degree - A track record of research and publication in one or more of the following areas: conversational artificial intelligence, machine learning, natural language processing, speech and signal processing, human-agent interactions, social robotics - Experience in teaching - An international postdoctoral experience is welcome but not mandatory - Excellent command of English 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: https://institutminestelecom.recruitee.com/o/enseignantchercheur-en-machine-learning-pour-la-modelisation-des-comportements-socioemotionnels-a-telecom-paris-cdi English Version: https://institutminestelecom.recruitee.com/l/en/o/enseignantchercheur-en-machine-learning-pour-la-modelisation-des-comportements-socioemotionnels-a-telecom-paris-cdi 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) [1] https://www.telecom-paris.fr/en/research/laboratories/information-processing-and-communication-laboratory-ltci/research-teams/signal-statistics-learning/social-computing [2] https://www.telecom-paris.fr/en/research/laboratories/information-processing-and-communication-laboratory-ltci/research-teams/signal-statistics-learning [3] https://www.telecom-paris.fr/fr/lecole/departements-enseignement-recherche/image-donnees-signal [4] https://www.telecom-paris.fr/en/research/laboratories/information-processing-and-communication-laboratory-ltci [5] https://www.telecom-paris.fr/en/home [6] https://www.telecom-paris.fr/en/the-school/teaching-research-departments/economics-and-social-sciences
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| 6-14 | (2023-02-08) Post-doctoral and engineer positions @ LORIA-INRIA Nancy, France Automatic speech recognition for non-natives speakers in a noisy environment
Post-doctoral and engineer positions
Starting date: beginning 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 in the models used by ASR.
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,
- the 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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| 6-15 | (2023-02-15) Ingenieur chef de projet, CRI Nancy, France
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| 6-16 | (20323-02-17) Internship Ingénieur.e de recherche NLP, LUNII, Paris
 TECH · LUNII PARIS · TÉLÉTRAVAIL HYBRIDE
Ingénieur.e de recherche NLP - [Stage - 6 mois]Lunii, c'est une aventure humaine et entrepreneuriale, lancée en août 2016 * Ma Fabrique à Histoires*, un objet littéraire, technologique et ludique pour les enfants de 3 à 8 ans.Vos missions Vous rejoindrez le Pôle Tech pour participer à un projet de recherche appliquée autour de la synthèse vocale narrative. La synthèse vocale a connu des avancées spectaculaires grâce à l’utilisation de réseaux de neurones profonds, mais les procédures de préparation et d’étiquetage des données d’apprentissage sont encore très chronophages. Pour répondre à cette problématique, vous contribuerez principalement à l’amélioration d’outils d’analyse et d’étiquetage automatique dans le cadre de la préparation d’un corpus de parole pour un système de synthèse vocale. Vous aurez pour missions de : 👩🎓 Étudier et améliorer les phonétiseurs - aligneurs existants
🙋 Évaluer et comparer les méthodes d’analyse structurelle d’une histoire
💃 Constituer un corpus de parole narrative
Liste non exhaustive. Lunii recrute et reconnaît tous les talents : nous sommes profondément attaché·e·s à la mixité et à la diversité, on vous attend ! Profil recherché
Process de recrutement Envoyez-nous votre CV, nous prenons ensuite le temps de bien étudier votre candidature et si elle correspond à l’offre, nos échanges (en visio ou présentiel) continuent :
Informations complémentaires
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| 6-17 | (2023-02-19) Two professorships @ Technische Universität Darmstadt, Germany Technische Universität Darmstadt is one of Germany’s leading technical universities with broad excellence in research, an interdisciplinary profile, and an explicit focus on engineering sciences.
The department of Electrical Engineering and Information Technology invites applications for a Full Professorship (W3) or Assistant Professorship (W2 with Tenure Track) for Signal Theory and Statistical Learning (Code No 6)
We are looking for an excellent researcher with credentials in at least one of the following areas: •Theory and methods of statistical inference •Robust statistical signal processing • Statistical learning theory •Theoretical performance analysis and interpretability of signal processing methods •Guarantees and interpretability of statistical learning methods •New trends in statistical signal and learning theory Application deadline: March 15, 2023 All further information about the position and application process can be found under: https://www.tu-darmstadt.de/universitaet/karriere_an_der_tu/stellenangebote/aktuelle_stellenangebote/stellenausschreibungen_detailansichten_1_502208.en.jsp
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| 6-18 | (2023-02-28) Maitre de conferences, Paris-Saclay/LISN, Orsay, France L'Université Paris-Saclay recrute un·e Maître·sse de Conférences en informatique (27ème section) pour la rentrée 2023. La personne recrutée travaillera au LISN, le Laboratoire Interdisciplinaire des Sciences du Numériques de l'Université Paris-Saclay, dans le département Science et Technologies des Langues (STL). Le profil recherche porte sur le traitement la langue multimodale. L'enseignement se fera à l'UFR de Sciences. N'hésitez pas à me contacter ou à contacter un membre du département Science et Technologies des Langues si vous avez des questions. Vous trouverez ci-dessous des détails sur les profils recherche et enseignement. Gilles Adda Enseignement
La personne recrutée pourra enseigner dans toutes les filières relevant du département informatique de la Faculté des Sciences d?Orsay, au niveau Licence et Master (classique et en apprentissage). Elle devra enseigner dans les domaines à renforcer en base de donnée et sciences des données. Elle pourra enseigner dans ses domaines d?intérêts bien entendu. L?enseignement constitue l?une des missions qui fonde l?université. Les questions de la qualité d?une formation dispensée et de la qualité des apprentissages des étudiants sont plus que jamais au c?ur des préoccupations de l?Université Paris Saclay. A ce titre, le profil enseignement de ce poste inclut une capacité à concevoir les séquences d?enseignement selon des objectifs d?apprentissage et des compétences explicites, et éventuellement à expérimenter des modalités pédagogiques innovantes. La personne recrutée sera également amenée à participer rapidement à la vie de l?établissement (gestion de filière, implication dans l?une des structures de l?université,?). Une expérience en termes de responsabilités collectives est vivement souhaitée. Le ou la candidate devra clairement indiquer son projet d'intégration en matière d'enseignement, dans le cadre de l'offre de formation de l'université et en accord avec le département Informatique de la Faculté des sciences. Recherche
La candidate ou le candidat développera ses activités de recherche au sein du Laboratoire Interdisciplinaire des sciences du numérique (LISN - UMR9015) implanté sur l'université Paris-Saclay. La candidate ou le candidat intégrera le département Sciences et Technologies des Langues (STL) et renforcera les activités orientées vers le traitement du langage multimodal. Le langage naturel inclut les modalités écrites, parlées et signées ; il peut également s'accompagner d?attitudes sociales et de dimensions non verbales. Le traitement du langage relève alors du traitement conjoint de multiples canaux d?informations. De plus, il est souvent utilisé pour décrire des concepts et désigner des entités qui sont essentiellement multimodales (description d'une image, d'un événement, etc.). Les sujets intéressant le laboratoire autour de cette problématique sont :
-- |Depuis le 1er janvier 2021, le LIMSI a fusionné avec le LRI et est devenu le LISN (Laboratoire Interdisciplinaire des Sciences du Numérique) |Since January 1st 2021, LIMSI merged with the LRI lab and became the LISN (Interdisciplinary Computer Science Laboratory) | - | |Gilles Adda | responsable du département Sciences et Technologies des Langues | head of Language Science and Technology Department | http://www.limsi.fr/Individu/gadda/ |
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| 6-19 | (2023-03-02) Full Professor in Computer Sciences @Grenoble-INP Phelma/Gipsa-Lab, Grenoble, France Grenoble-INP Phelma is recruiting in 2023 a Full Professor in Computer Sciences (Section CNU 27). The host research laboratory will be GIPSA-lab (UMR 5216). The research profile is entitled 'Computer Science and Learning for Image and Signal Processing' and covers all the scientific themes of GIPSA-lab related to information processing, including automatic speech and language processing, for an affiliation to the « Speech and Cognition » group and the CRISSP (Cognitive Robotics, Interactive Systems and Speech Processing) team. The job description is available at https://phelma.grenoble-inp.fr/fr/l-ecole/concours-enseignants-chercheurs-2023
Contacts : Nicolas Marchand, Laurent Girin, Thomas Hueber (firstname.lastname@gipsa-lab.grenoble-inp.fr)
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| 6-20 | (2023-03-15) PhD student in Phonetics, Stockholm, SwedenPhD student in PhoneticsStockholm Ref. No. SU FV-0793-23
at the Department of linguistics. Closing date: 15 april 2023. Project description Qualification requirements Selection
In selecting applicants for postgraduate education in linguistics, the department board must take into account rules and regulations of the Faculty of Humanities. In addition to the above selection criteria, the following will be of great importance in the assessment:
Admission Regulations for Doctoral Studies at Stockholm University are available at: www.su.se/rules and regulations. Terms of employment The term of the initial contract may not exceed one year. The employment may be extended for a maximum of two years at a time. However, the total period of employment may not exceed the equivalent of four years of full-time study. Doctoral students should primarily devote themselves to their own education, but may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position. For this particular position, the doctoral student is expected to perform departmental duties corresponding to 20 % of full time. Where applicable, the total time of the appointment is extended to correspond to a full-time doctoral programme for four years. Please note that admission decisions cannot be appealed. Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all. Contact Union representatives Application Please include the following information with your application
and, in addition, please include the following documents
Note that the proposal must address the following questions: why your project is suitable to be carried out at the Department of Linguistics at Stockholm University, how you intend to contribute to the research environment at the department with your research project, what makes you particularly suitable (to carry out the proposed research project).
You are welcome to apply! Stockholm University contributes to the development of sustainable democratic society through knowledge, enlightenment and the pursuit of truth.
Closing date: 15/04/2023
URL to this page
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| 6-21 | (2023-03-06) 2 open postdoc position at the LISN (ex-LIMS), Paris, France We have currently 2 open postdoc position at the LISN (ex-LIMS). You can apply online
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| 6-22 | (2023-03-08) PhD in ML/Speech Processing @LIA, Avignon, France PhD in ML/Speech Processing ? Speaker recognition systems against voice attacks : voices synthesis and voice transformation
Starting date: September 1st, 2023 (flexible)
Application deadline: July 10th, 2023
Interviews (tentative): July 15th, 2023
Salary: ~2000? gross/month (social security included)
Mission: research oriented (teaching possible but not mandatory)
Keywords: speech processing, automatic speaker recognition, anti-spoofing, deep neural network
CONTEXT
It is now widely accepted that automatic speaker recognition (ASV) systems are vulnerable not only to speech produced artificially by text-to-speech (TTS) [1], but also to other forms of attacks such as voice conversion (VC) and replay [2]. Voice conversion can be used to manipulate the voice identity of a speech signal, has progressed extremely rapidly in recent years [3], and has indeed become a serious threat.
The progress made in recent years in deep neural networks training has enabled spectacular advances in the fields of text-to-speech (TTS) and voice conversion (VC): DeepVoice, Tacotron 1 and 2 [4], Auto-VC [5,6]. Existing architectures now make possible producing synthesized or manipulated artificial voices with a realism close to or equal to that of human voices [4]. At the same time, voice conversion algorithms (from one speaker to another) have also made spectacular advances. It now becomes possible to clone a voice identity using a small amount of data. In the space of two years, extremely significant advances have been made [5,6,7]. The ability of these algorithms to forge voice identities capable of deceiving speaker recognition and counter-measure systems is an urgent topic of research.
Progress in terms of the fight against identity theft has been led by the initiative of the ASVspoof community, formed in 2013 and recognized as competent at the international level [8]. The most significant efforts have been made at the level of the acoustic parametrization (front-end) making it possible to better differentiate authentic (human) utterances from fraudulent utterances. The best performing system [9], which combines acoustic parameters based on Cepstrum-Mel, Cepstrum based on cochlear filters and instantaneous frequencies using a classifier based on a Gaussian mixture model, obtained the best performance.
For the past years, research efforts have focused on the back-end. As in speaker recognition research, the anti-spoofing community has embraced the power of deep learning and, unsurprisingly, the neural architectures used are almost the same. Advances in anti-spoofing have followed the rapid advances in TTS and VC. The best anti-spoofing system again used traditional acoustic parameters, with a classifier based on ResNet-18 [10].
SCIENTIFIC OBJECTIVES
As part of this thesis, the robustness of existing countermeasures against new forms of adversarial attacks designed specifically to deceive them will be assessed. One of the advances expected in this thesis will focus on the design of new countermeasures to detect such emerging, increasinly adversarial attacks. To do this, two avenues will be explored. The first is to redesign front-end feature extraction to capture cues that characterize adversarial attacks, then use them with re-trained classifiers. As it is not always easy to identify reliable characteristics, the second direction will aim at the adoption of end-to-end architectures able to learn characteristics automatically. Although these advances improve robustness to adversarial attacks, it will be important to ensure that the resulting countermeasures remain robust to previous attacks. This is known as the problem of the generalization. An effective anti-spoofing countermeasure must reliably detect any form of attack it encounters, not just the specific attacks it is trained to detect. Finally, improving adversarial attack detection performance should not come at the cost of increased false positives (genuine speech labeled as spoofed speech), which can hurt usability and convenience. The progress and results targeted in this thesis will therefore be countermeasures capable of defending speaker recognition systems against adversarial and non-adversarial attacks.
In parallel to this competition between research teams specializing in attacks and research teams specializing in counter-attacks, the speaker recognition community is focused on the creation and design of high-performance systems that are robust to acoustic variability. Recognition systems are trained to recognize speakers in increasingly difficult conditions (presence of several types of noise: additive, reverb, etc.). This robustness against difficult acoustic conditions can lead to weakness against recordings of attacks that were not taken into account during training. Of course this vulnerability can be reduced by using countermeasures (CM) systems. This approach can impact the usability of ASV systems since the countermeasures can also reject genuine clients (authentic users). This thesis will therefore go beyond the state of the art by optimizing both the ASV and the CM system, so that they work together to achieve the best possible compromise between security and usability/convenience.
REQUIRED SKILLS
- Master 2 in speech processing, computer science or data science
- Good mastering of Python programming and deep learning framework
- Previous experience in bias in machine learning would be a plus
- Good communication skills in English
- Good command of French would be a plus but is not mandatory
LAB AND SUPERVISION
The PhD position will be co-supervised by Nicholas Evans from EURECOM and Driss Matrouf from LIA-Avignon. Joint meetings are planned on a regular basis and the student is expected to spend time in LIA-Avignon. The students, along with the partners (IRCAM specialized in attack generation and EURECOM specialized in countermeasures) will closely collaborate.
INSTRUCTIONS FOR APPLYING
Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Driss Matrouf (driss.matrouf@univ-avignon.fr), Mickael Rouvier (mickael.rouvier@univ-avignon.fr) and Nicholas Evans (evans@eurecom.fr).
REFERENCES
[1] https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-1567157402
[2] N. Evans, T. Kinnunen and J. Yamagishi, ?Spoofing and countermeasures for automatic speaker verification? in Proc. Interspeech 2013 Aug 25 (pp. 925-929).
[3] Z. Yhi et al. (2020) Voice Conversion Challenge 2020- Intra-lingual semi-parallel and cross-lingual voice conversion . SCA Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020.
[4] J. Shen et al, Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Prediction, ICASSP,2018.
[5] Qian, K., Zhang, Y., Chang, S., Yang, X., and Hasegawa- Johnson, M. AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss. In International Conference on Machine Learning (ICML), pp. 5210?5219, 2019
[6] Zhang, J.-X., Ling, Z.-H., and Dai, L.-R. Non-Parallel Sequence-to-Sequence Voice Conversion With Disentan- gled Linguistic and Speaker Representations. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 28:540?552, 2020.
[7] Jia, Y., Zhang, Y., Weiss, R., Wang, Q., Shen, J., Ren, F., ... & Wu, Y. (2018). Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In Advances in neural information processing systems (pp. 4480-4490).
[8] N. Evans, T. Kinnunen and J. Yamagishi, ?Spoofing and countermeasures for automatic speaker verification? in Proc. Interspeech 2013,
pp. 925-929, 2013
[9] T. B. Patel, H. A. Patil, ?Combining Evidences from Mel Cepstral, Cochlear Filter Cepstral and Instantaneous Frequency Features for Detection of Natural vs. Spoofed Speech?, in Proc. INTERSPEECH 2015, pp. 2062-2066, 2015
[10] X. Cheng, M. Xu, and T. F. Zheng, ?Replay detection using CQTbased modified group delay feature and ResNeWt network in ASVs poof 2019?, in Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 540?545, 2019
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| 6-23 | (2023-03-09) Doctoral position : Acoustic to Articulatory Inversion by using dynamic MRI images @LORIA, Nancy, France Doctoral position : Acoustic to Articulatory Inversion by using dynamic MRI images
Loria ?Lorraine Research Laboratory in Computer Science and its Applications? is a research unit common to CNRS, the Université de Lorraine and INRIA. Loria gathers 450 scientists and its missions mainly deal with fundamental and applied research in computer sciences, especially the MultiSpeech Team which focuses automatic speech processing, audiovisual speech and speech production. IADI is a research unit common to Inserm the Université de Lorraine whose specialty is developing various techniques and methods to improve imaging of moving organs via the acquisition of MR images.
This PhD project founded by LUE (Lorraine Université d?Excellence) associates the Multispeech team and the IADI laboratory.
Start date is (expected to be) 1st septembre 2023 or as soon as possible thereafter.
Supervisors Yves Laprie, email yves.laprie@loria.fr Pierre-André Vuissoz, email pa.vuissoz@chru-nancy.fr
The project
Articulatory synthesis mimics the speech production process by first generating the shape of the vocal tract from the sequence of phonemes to be pronounced, then the acoustic signal by solving the aeroacoustic equations. Compared to other approaches to speech synthesis which offer a very high level of quality, the main interest is to control the whole production process, beyond the acoustic signal alone. The objective of this PhD is to succeed in the inverse transformation, called acoustic to articulatory inversion, in order to recover the geometric shape of the vocal tract from the acoustic signal. A simple voice recording will allow the dynamics of the different articulators to be followed during the production of the sentence. Beyond its interest in terms of scientific challenge, articulatory acoustic inversion has many potential applications. Alone, it can be used as a diagnostic tool to evaluate articulatory gestures in an educational or medical context.
Description of work
The objective is the inversion of the acoustic signal to recover the temporal evolution of the medio-sagittal slice. Indeed, dynamic MRI provides two-dimensional images in the medio-sagittal plane at 50Hz of very good quality and the speech signal acquired with an optical microphone can be very efficiently deconstructed with the algorithms developed in the MultiSpeech team (examples available on https://artspeech.loria.fr/resources/). We plan to use corpora already acquired or in the process of being acquired. These corpora represent a very large volume of data (several hundreds of thousands of images) and an approach for tracking the contours of articulators in MRI images which gives very good results was developed to process corpora. The automatically tracked contours can therefore be used to train the inversion. The goal is to perform the inversion using the LSTM approach on data from a small number of speakers for which sufficient data exists. This approach will have to be adapted to the nature of the data and to be able to identify the contribution of each articulator. In itself, successful inversion to recover the shape of the vocal tract in the medio-sagittal plane will be a remarkable success since the current results only cover a very small part of the vocal tract (a few points on the front part of the vocal tract). However, it is important to be able to transpose this result to any subject, which raises the question of speaker adaptation, which is the second objective of the PhD.
What we offer
Supervisors Yves Laprie, email yves.laprie@loria.fr Pierre-André Vuissoz, email pa.vuissoz@chru-nancy.fr
Application Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below. Please include:
log into Inria?s recruitment system(https://jobs.inria.fr/public/classic/en/offres/2023-05790) in order to apply to this position.
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| 6-24 | (2023-03-10) Ingénieur d'étude, GIPSA LAB, Grenoble, France Le service Plateformes du laboratoire GIPSA-LAB (CNRS/G-INP/UGA, Grenoble) recrute un.e ingénieur.e d?étude en instrumentation. Pour plus d'information, la fiche de poste détaillée est disponible ici : https://filesender.renater.fr/?s=download&token=2ae39938-121d-44c4-b6ce-385029ecf331 N'hésitez pas à me contacter si besoin d'information complémentaire. Coriandre VILAIN -- Coriandre Vilain Ingénieur de Recherche UGA Equipe PCMD, Pôle Parole & Cognition, GIPSA-LAB -- Responsable Service Plateformes GIPSA -- 04 76 82 77 80 www.gipsa-lab.fr/~coriandre.vilain
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| 6-25 | (2023-03-12) Associate-Assistant Professor in artificial intelligence for semantic and multi-modal multimedia analysis. Telecom Sud Paris, France Dear All, Telecom SudParis welcomes applications for a permanent position of Associate-Assistant Professor in artificial intelligence for semantic and multi-modal multimedia analysis. Telecom SudParis is a public graduate school for engineering, which has been recognized on the highest level in the domain of digital technology. Telecom SudParis is co-founder member of the Institut Polytechnique de Paris and part of the Institut Mines-Telecom, the number one group of engineering schools in France. The recruited assistant/associate professor will join the ARTEMIS (Advanced Research and TEchniques for Multidimensional Imaging Systems) Department of Télécom SudParis and the SAMOVAR laboratory. The targeted research theme concerns the field of artificial intelligence, applied to the semantic analysis of massive, distributed and heterogeneous multimedia data. This concerns the automatic, multi-modal interpretation of complex audio-visual documents, computer vision, multimedia indexation, knowledge extraction and machine learning methodologies. The expected contributions will focus on deep neural network learning methods and target the entire multimedia content processing chain. Detailed information can be found at the following URL: https://institutminestelecom.recruitee.com/l/en/o/maitre-de-conferences-en-intelligence-artificielle-pour-lanalyse-semantique-de-donnees-multimedia-cdi The application deadline is March 31, 2023. Please do not hesitate to contact me for any further information. Best regards, Titus ZAHARIA, Professor Head of the ARTEMIS Department Télécom SudParis Institut Polytechnique de Paris titus.zaharia@telecom-sudparis.eu
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| 6-26 | (2023-03-16) PhD Position in Deep Cascaded Representation Learning for Speech Modelling, Univ.Sheffield, UK Title of Project: Deep Cascaded Representation Learning for Speech Modelling Supervisor:Professor Thomas Hain Deadline for Applications:13th April 2023
The successful applicant will work under the supervision of Prof. Hain who is the Director of the
Funding Details:
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| 6-27 | (2023-03-15) Research Associate in Integrated Multitask Neural Speech Labelling, Univ.Sheffield, UK Job Title:Research Associate in Integrated Multitask Neural Speech Labelling We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total
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| 6-28 | (2023-03-15) PhD Position in Adaptive Deep Learning for Speech and Language, Univ.Sheffield, UK Title of Project: PhD Position in Adaptive Deep Learning for Speech and Language Supervisor:Professor Thomas Hain
The successful applicant will work under the supervision of Prof. Hain who is the Director of the
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| 6-29 | (2023-03-17) Deux postes de MCF en phonétique au concours à l'Université Paul-Valéry Montpellier 3 ,France Deux postes de MCF en phonétique sont ouverts au concours à l'Université Paul-Valéry Montpellier 3 cette année : Phonétique générale et traitement outillé de l'oral (UMR 5267 Praxiling) : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/ANTEE/2023_1/0341089Z/FOPC_0341089Z_4352.pdf Phonétique et Didactique de l'oral en FLE (acquisition / appropriation des langues) : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/ANTEE/2023_1/0341089Z/FOPC_0341089Z_4353.pdf Date limite : 30 mars 2023
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| 6-30 | (2023-03-20) 2 postdocs for project ASTRID DeTOX @IRCAM Paris and EURECOM Sophia Antipolis , France Dans le cadre du projet ASTRID DeTOX sur la lutte contre les vidéos hyper-truquées de personnalités françaises,
deux postes sont à pourvoir :
- Un post-doc de 15 mois à l?IRCAM sur la génération de deep fakes audio-visuels
- Un post-doc de 18 mois ou une thèse de 36 mois à EURECOM sur la détection de deep fakes audio-visuels
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| 6-31 | (2023-03-15) PhD student in Phonetics, Stockholm University, SwedenPhD student in PhoneticsStockholm Ref. No. SU FV-0793-23
at the Department of linguistics. Closing date: 15 april 2023. Project description Qualification requirements Selection
In selecting applicants for postgraduate education in linguistics, the department board must take into account rules and regulations of the Faculty of Humanities. In addition to the above selection criteria, the following will be of great importance in the assessment:
Admission Regulations for Doctoral Studies at Stockholm University are available at: www.su.se/rules and regulations. Terms of employment The term of the initial contract may not exceed one year. The employment may be extended for a maximum of two years at a time. However, the total period of employment may not exceed the equivalent of four years of full-time study. Doctoral students should primarily devote themselves to their own education, but may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position. For this particular position, the doctoral student is expected to perform departmental duties corresponding to 20 % of full time. Where applicable, the total time of the appointment is extended to correspond to a full-time doctoral programme for four years. Please note that admission decisions cannot be appealed. Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all. Contact Union representatives Application Please include the following information with your application
and, in addition, please include the following documents
Note that the proposal must address the following questions: why your project is suitable to be carried out at the Department of Linguistics at Stockholm University, how you intend to contribute to the research environment at the department with your research project, what makes you particularly suitable (to carry out the proposed research project).
You are welcome to apply! Stockholm University contributes to the development of sustainable democratic society through knowledge, enlightenment and the pursuit of truth.
Closing date: 15/04/2023
URL to this page
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| 6-32 | (2023-03-20) PhD student position in experimental phonetics @ Stockholm University, Sweden The Department of Linguistics at Stockholm University invites applications for a PhD student position in experimental phonetics, including (but not limited to) topics in prosody. For details, see: https://www.su.se/english/about-the-university/work-at-su/available-jobs/phd-student-positions-1.507588?rmpage=job&rmjob=20262&rmlang=UK .
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| 6-33 | (2023-03-20) PhD student, Bielefeld University, Germany The Digital Linguistics Lab at Bielefeld University (head: JProf. Dr.-Ing. Hendrik Buschmeier) is seeking to fill a research position (PhD-student, E13 TV-L, 100%, fixed-term) in the area of multimodal human-robot interaction in the research project ?Hybrid Living?.
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| 6-34 | (2023-03-24) Research assistant, McGill University, Montreal, Canada We are seeking a multimodal designer to take on a central role in the Shared Reality Lab?s open source IMAGE project (image.a11y.mcgill.ca), focused on making photos, charts, and maps available to people who are blind or low vision. We are currently operating under two grants, focused on integrating haptic force feedback and pin array devices into IMAGE. You will work with a multidisciplinary team of user experience researchers, designers, and developers who will support you in designing and releasing multimodal audio and haptic experiences that will delight our end users. The primary requirement is a strong background and passion for owning both design and iterative testing of combined audio/haptic end-user experiences. Since the goal of IMAGE is to release a practical solution that can be used on a daily basis, the candidate will work directly with developers and the rest of the team to make sure that ideas and designs get translated into implementable requirements, then deployed into production.
Other useful skills (not required). If the candidate has the desire and capability, they are also welcome to participate in software architecture and implementation, for example:
Candidates applying as a research assistant must be eligible to work in Canada.
The position is available immediately, with an initial appointment of up to one year. Informal inquiries are welcome.
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| 6-35 | (2023-04-07) Researcher position at the School of Informatics, Kyoto University , Japan
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| 6-36 | (2023-04-08) PhD Position @ SPEAC, Radboud University, Nijmegen, The Netherlands We have an open 4yr PhD position in the SPEAC labhttps://hrbosker.github.io (Speech Perception in Audiovisual Communication) at the Donders Institute, Radboud University, Nijmegen, The Netherlands.
The position is funded through an ERC Starting Grant (HearingHands, 101040276) awarded to Dr. Hans Rutger Bosker. We are looking for candidates with a strong background in speech perception and an interest in audiovisual prosody and gesture-speech integration.
You will work closely with Dr. Hans Rutger Bosker (PI) and Prof. James McQueen (promotor). The PhD project aims to determine how and when the timing of seemingly meaningless up-and-down hand gestures influences audiovisual speech perception, specifically targeting more naturalistic communicative settings. You will use virtual avatars, allowing careful control of their gestural movements, to establish which kinematic and communicative properties of hand gestures influence low-level speech perception. You will assess how challenging listening conditions impact the perceptual weighting of visual, auditory and audiovisual cues to prominence, as well as determine the time-course of these cues using eye-tracking. Finally, you will design training studies to test how humans adjust their perception to between-talker variation in gesture-speech alignment.
- 4 year contract, 1.0 FTE - gross monthly salary: ? 2,541 - ? 3,247 (scale P) - application deadline: May 22, 2023 - preferred starting date: September 1, 2023
More details about the project, profile, and what we have to offer is available through the link below. If you have any questions, do get in touch at HansRutger.Bosker@donders.ru.nl
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| 6-37 | (2023-04-09) Postdoc @ University of Washington, USA University of Washington, Seattle, WA, USA Laboratory for Speech Physiology and Motor Control
Post-doctoral position in speech sensorimotor learning in typical adults, DBS patients, and adults who stutter
The Laboratory for Speech Physiology and Motor Control (PI Ludo Max, Ph.D.) at the University of Washington (Seattle) is seeking to fill a post-doctoral position in the areas of sensorimotor integration and sensorimotor learning for speech production. The position will involve experimental work on sensorimotor adaptation, sensory prediction, and error evaluation in typical adults, Parkinson’s and essential tremor patients with deep brain stimulation implants (DBS), and adults who stutter. The lab is located in the University of Washington's Department of Speech and Hearing Sciences and has additional affiliations with the Graduate Program in Neuroscience, the Department of Bioengineering, and the Department of Linguistics.
The appointment is initially for one year, with renewal possible contingent upon satisfactory performance and productivity. We are looking for a candidate available to start in the summer of 2023, and applicants should have completed all requirements for their Ph.D. degree by that time. Review of applications will begin immediately. Candidates with a Ph.D. degree in neuroscience, cognitive/behavioral neuroscience, motor control/kinesiology, biomedical engineering, communication disorders/speech science, and related fields, are encouraged to apply.
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| 6-38 | (2023-04-12) 3 Postdocs @ IRIT, Toulouse, France Dans le cadre d'un projet PIA, nous avons trois offres de post-doc d'une durée de 2 ans,
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| 6-39 | (2023-04-15) Chargé.e de recherche et développement (H/F) projet Bruel, IRCAM, Paris, France Offre d’emploi : 1 Chargé.e de recherche et développement (H/F) Conversion neuronale de l’identité vocale pour la réalisation d’attaques adverses Disponibilité et durée : 18 mois, de préférence à partir du 01 juin 2023 Description du poste: Dans le cadre du projet ANR BRUEL (2022-2026), l’équipe Analyse et Synthèse des sons recherche un.e chargé.e de recherche pour la conception, l’implémentation, et l’apprentissage d’algorithmes de conversion neuronale de l’identité vocale pour la création d’attaques d’usurpation d’identité. A partir d’un ensemble de scénarios d’attaques envisagées pour réaliser ces attaques en fonction des moyens et ressources disponibles (expertise, algorithmes, données), les travaux consisteront dans un premier temps à réaliser l’implémentation d’un banc d’essais d’algorithmes pour évaluer la robustesse des systèmes d’authentification et de détection face à ces attaques. Les travaux porteront dans un second temps sur l’une ou plusieurs des problématiques suivantes : - L’apprentissage de la conversion d’identité à partir de données de qualité hétérogène et dégradée (compression, bruits, etc…) librement accessibles (par exemple sur internet), et le transfert d’identité à partir de peu de données par des stratégies d’adaptation neuronale à partir de peu d’exemples; - La génération de conversions avec un contrôle de l’emprunte acoustique pour que l’attaque soit adaptée à l’environnement sonore et au canal de communication en fonction des scénarios envisagés (depuis des conditions professionnelles jusqu’à des conditions dégradées de communication téléphonique ou internet). L’ensemble des travaux réalisés seront évalués selon les protocoles usuels en conversion d’identité vocale, mais également en relation avec les partenaires du projet pour mesurer les performances des systèmes d’authentification/détection en fonction des scénarios envisagés. Les avancées réalisées seront intégrées au système de conversion neuronale de l'identité vocale de l’Ircam et évaluées in situ dans le cadre de productions professionnelles et/ou artistiques réalisées à l’Ircam. Le.a chargé.e de recherche collaborera également avec l’équipe de développement et participera aux activités du projet (évaluation des algorithmes, réunions, spécifications, livrables, rapports). Présentation du projet BRUEL Le projet ANR BRUEL (ElaBoRation d’Une méthodologie d’EvaLuation des systèmes d’identification par la voix) concerne l’évaluation/certification des systèmes d’identification par la voix face aux attaques adverses. En effet, les systèmes de reconnaissance automatique du locuteur sont vulnérables non seulement à la parole produite artificiellement par synthèse vocale, mais aussi à d'autres formes d'attaques telles que la conversion d’identité vocale et la relecture. Les artefacts créés lors de la création ou la manipulation de ces attaques frauduleuses constituent les marques laissées dans le signal par les algorithmes de synthèse vocale permettant ainsi de distinguer la voix réelle originale d’une voix usurpée. Dans ces conditions, la détection de l'usurpation d'identité requiert d'évaluer les contre-mesures d'usurpation d'identité en même temps que les systèmes de reconnaissance du locuteur. Le projet BRUEL ambitionne de proposer la première méthodologie d’évaluation/certification des systèmes d'identification par la voix basée sur une approche Critères Communs. Contexte de travail Le travail sera effectué à l’IRCAM au sein de l’équipe Analyse et Synthèse des sons encadré par Nicolas Obin et Axel ROEBEL (SU, CNRS, IRCAM). Le travail pourra être mené partiellement à distance, avec la nécessité d’une participation aux réunions d’avancement du projet. L'Ircam est une association à but non lucratif, associée au Centre National d'Art et de Culture Georges Pompidou, dont les missions comprennent des activités de recherche, de création et de pédagogie autour de la musique du XXème siècle et de ses relations avec les sciences et technologies. Au sein de l'unité mixte de recherche, UMR 9912 STMS (Sciences et Technologies de la Musique et du Son) commune à l’Ircam, à Sorbonne Université, au CNRS, et au Ministère de la Culture et de la Communication, des équipes spécialisées mènent des travaux de recherche et de développement informatique dans les domaines de l'acoustique, du traitement des signaux sonores, des sciences cognitives, des technologies d’interaction, de l’informatique musicale et de la musicologie. L'Ircam est situé au centre de Paris à proximité du Centre Georges Pompidou au 1, Place Stravinsky 75004 Paris. Expérience et compétences requises: Nous recherchons un.e candidat.e spécialisé.e en apprentissage de réseaux de neurones profonds et en traitement automatique de la parole ou en vision, de préférence en deep fakes. Le·a candidate devra avoir une thèse de doctorat en sciences informatiques dans les domaines de l’apprentissage par réseaux de neurones profonds, ainsi que des publications dans des conférences et revues reconnues dans le domaine. Le·a candidat·e idéal·e aura: • Une solide expertise en apprentissage machine, et en particulier en réseaux de neurones profonds. • Une bonne expérience en traitement automatique de la parole ; de préférence dans le domaine de la génération ou des deep-fakes; • Maîtrise du traitement du signal audio-vidéo numérique; • Une excellente maîtrise du langage de programmation Python, de l’environnement TensorFlow pour l’apprentissage de réseaux de neurones, et du calcul distribué sur des serveurs GPUs • Excellente maîtrise de l’anglais scientifique parlé et écrit • Autonomie, travail en équipe, productivité, rigueur et méthodologie Salaire Selon formation et expérience professionnelle Candidatures Prière d'envoyer une lettre de motivation et un CV détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi que tout autre information pertinente) à Nicolas.Obin@ircam.fr et Axel.Roebel@ircam.fr Date limite de candidature 31 mai 2023
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| 6-40 | (2023-04-15) Chargé.e de recherche et développement (H/F) projet DeTOX, IRCAM, Paris, France Offre d’emploi : 1 Chargé.e de recherche et développement (H/F) Génération de deep fakes audio-visuel Disponibilité et durée : 15 mois, de préférence à partir du 01 juin 2023 Description du poste Dans le cadre du projet ASTRID DeTOX (2023-2025), l’équipe Analyse et Synthèse des sons recherche un.e chargé.e de recherche pourl’implémentation et l’apprentissage d’algorithmes pour la génération de deep fakes audio-visuels avec les missions suivantes : - Collection, implémentation, et apprentissage d’algorithmes représentatifs de l’état-del’art pour la génération de deep fakes audio et visuel - Implémentation d’un nouvel algorithme de génération de deep fakes audio-visuel avec synchronisation des deux modalités en particulier pour assurer la cohérence de la parole et du mouvement du lèvre et du bas du visage - Construction de bases de données audio-visuel des personnes ciblées et apprentissage de modèles de génération de deep fakes pour ces personnes Le.a chargé.e de recherche collaborera également avec l’équipe de développement et participera aux activités du projet (évaluation des algorithmes, réunions, spécifications, livrables, rapports). Présentation du projet DeTOX Les récents challenges ont montré qu'il était extrêmement difficile de mettre au point des détecteurs universels de vidéos hyper-truquées - à l'exemple des 'deep fakes' utilisés pour contrefaire l'identité d'une personne. Lorsque les détecteurs sont exposés à des vidéos générées par un algorithme nouveau, c'est-à-dire inconnu lors de la phase d'apprentissage, les performances sont encore extrêmement limitées. Pour la partie vidéo, les algorithmes examinent les images une par une, sans tenir compte de l'évolution de la dynamique faciale au cours du temps. Pour la partie vocale, la voix est générée de manière indépendante de la vidéo ; en particulier, la synchronisation audio-vidéo entre la voix et les mouvements des lèvres n'est pas prise en compte. Ceci constitue un point faible important des algorithmes de génération de vidéos hyper-truquées. Le présent projet vise à implémenter et à apprendre des algorithmes de détection de deepfakes personnalisés sur des individus pour lesquels on peut disposer et/ou fabriquer de nombreuses séquences audio-vidéo réelles et falsifiées. En se basant sur des briques technologiques de base en audio et vidéo récupérées de l'état de l'art, le projet se concentrera sur la prise en compte de l'évolution temporelle des signaux audio-visuels et de leur cohérence pour la génération et la détection. Nous souhaitons ainsi démontrer qu'en utilisant simultanément l’audio et la vidéo et en se focalisant sur une personne précise lors de l'apprentissage et de la détection, il est possible de concevoir des détecteurs efficaces même face à des générateurs encore non répertoriés. De tels outils permettront de scruter et de détecter sur le web d'éventuelles vidéos hyper-truquées de personnalités françaises importantes (président de la république, journalistes, chef d'étatmajor des armées, ...) et ce dès leur publication. Contexte de travail Le travail sera effectué à l’IRCAM au sein de l’équipe Analyse et Synthèse des sons encadré par Nicolas Obin et Axel ROEBEL (SU, CNRS, IRCAM). Le travail pourra être mené partiellement à distance, avec la nécessité d’une participation aux réunions d’avancement du projet. L'Ircam est une association à but non lucratif, associée au Centre National d'Art et de Culture Georges Pompidou, dont les missions comprennent des activités de recherche, de création et de pédagogie autour de la musique du XXème siècle et de ses relations avec les sciences et technologies. Au sein de l'unité mixte de recherche, UMR 9912 STMS (Sciences et Technologies de la Musique et du Son) commune à l’Ircam, à Sorbonne Université, au CNRS, et au Ministère de la Culture et de la Communication, des équipes spécialisées mènent des travaux de recherche et de développement informatique dans les domaines de l'acoustique, du traitement des signaux sonores, des sciences cognitives, des technologies d’interaction, de l’informatique musicale et de la musicologie. L'Ircam est situé au centre de Paris à proximité du Centre Georges Pompidou au 1, Place Stravinsky 75004 Paris. Expérience et compétences requises Nous recherchons un.e candidat.e spécialisé.e en apprentissage de réseaux de neurones profonds et en traitement automatique de la parole ou en vision, de préférence en deep fakes. Le·a candidate devra avoir une thèse de doctorat en sciences informatiques dans les domaines de l’apprentissage par réseaux de neurones profonds, ainsi que des publications dans des conférences et revues reconnues dans le domaine. Le·a candidat·e idéal·e aura: • Une solide expertise en apprentissage machine, et en particulier en réseaux de neurones profonds. • Une bonne expérience en traitement automatique de la parole ou en vision ; de préférence dans le domaine des deep-fakes; • Maîtrise du traitement du signal audio-vidéo numérique; • Une excellente maîtrise du langage de programmation Python, de l’environnement TensorFlow pour l’apprentissage de réseaux de neurones, et du calcul distribué sur des serveurs GPUs • Excellente maîtrise de l’anglais scientifique parlé et écrit • Autonomie, travail en équipe, productivité, rigueur et méthodologie Salaire Selon formation et expérience professionnelle Candidatures Prière d'envoyer une lettre de motivation et un CV détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi que tout autre information pertinente) à Nicolas.Obin@ircam.fr et Axel.Roebel@ircam.fr Date limite de candidature 31 mai 2023
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| 6-41 | (2023-05-09) PhD position @ IMT Brest France and Instituto Superior Técnico Lisbon, Portugal PhD Title: SUMMA-Sound : SUMMarization of Activities of daily living using Sound-based activity recognition Partnership: IMT Atlantique : Campus ☒ Brest ☐ Nantes ☐ Rennes Laboratory : Lab-STICC Doctoral school : ☒ SPIN ☐ 3MG Funding: IMT Atlantique, co-tutelle with Instituto Superior Técnico Context : IMT Atlantique, internationally recognised for the quality of its research, is a leading general engineering school under the aegis of the French Ministry of Industry and Digital Technology, ranked in the three main international rankings (THE, SHANGHAI, QS). Located on three campuses, Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international scale. With 290 researchers and permanent lecturers, 1000 publications and 18 M€ of contracts, it supervises 2300 students each year and its training courses are based on cutting-edge research carried out within 6 joint research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the team RAMBO (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) and of the laboratory Lab-STICC and the department of Computer Science of IMT Atlantique. Scientific context: The objective of this thesis is to develop a method for collecting and summarizing domestic health-related data relevant for medical diagnosis, in a non-intrusive manner using audio information. This research addresses the lack of existing practical tools for providing high-level succinct information to medical staff on the evolution of patients they follow for health diagnostic purposes. This research is based on the assumption that valuable diagnostic data can be collected by observing short- and long-term lifestyle changes and behavioural anomalies. It relies on the latest advances in the domains of audio-based activity recognition, summarization of human activity, and health diagnosis. Research on health diagnosis in domestic environments has already explored a variety of sensors and modalities for gathering data on human health indicators [5]. Nevertheless, audio-based activity recognition is notable for its less intrusive nature. Employing state-of-the-art sound-based activity recognition models [2] to monitor domestic human activity, the thesis will investigate and develop methods for summarization of human activity [3] in a human-understandable language, in order to produce easily interpretable data by doctors who, remotely, monitor their patients [4]. This work continues and fosters the research of the RAMBO team at IMT Atlantique on ambient systems, enabling well ageing at home for the elderly adults or dependent populations [1]. We expect this thesis to provide technology likely to relieve the burden on gerontologists and elderly-care facilities, and alleviate the caregiver shortage by offering some automatic support to the task of monitoring elderly or handicapped people, enabling them to age-at-home while still being followed by medical specialists using automated means. Expected contributions of the thesis Scientific goals: (1) Determine the set of human activities relevant for health diagnosis, (2) Implement a state-of-the-art model for audio-based activity recognition and validate its function by clinicians, (3) Develop a model for summarizing the evolution of human activity over time intervals of arbitrary duration (typically spanning from days to months and possibly years). Expected outcomes of the PhD: (1) A model for semantic summarization of human activity, based on sound recognition of activities of daily living. (2) A proof of concept for this model Candidate profile and required skills: • Master Degree in Computer Science (or equivalent) • Programming and Software Engineering skills (Python, Git, Software Architecture Design) • Data science skills • Machine learning skills • English speaking and writing skills References: [1] Damien Bouchabou. “Human activity recognition in smart homes : tackling data variability using context-dependent deep learning, transfer learning and data synthesis”. Theses. Ecole nationale supérieure Mines-Télécom Atlantique, May 2022. url: https://theses.hal.science/tel-03728064. [2] Detection and Classification of Acoustic Scenes and Events (DCASE). url: https://dcase.community/challenge2022/task-soundevent-detection-in-domestic-environments (visited on 07/01/2022). [3] P Durga et al. “When less is better: A summarization technique that enhances clinical effectiveness of data”. In: Proceedings of the 2018 International Conference on Digital Health. 2018, pp. 116–120. [4] Akshay Jain et al. “Linguistic summarization of in-home sensor data”. In: Journal of Biomedical Informatics 96 (2019), p. 103240. issn: 1532-0464. [5] Mostafa Haghi Kashani et al. “A systematic review of IoT in healthcare: Applications, techniques, and trends”. In: Journal of Network and Computer Applications 192 (2021), p. 103164. Work Plan: The thesis will be organised in the following steps: (1) Definition of pertinent sounds and activities for health diagnosis, (2) Hardware set-up, (3) Dataset constitution, (4) Activity recognition, (5) Diarization of activities, (6) Summarization, (7) Validation in a real environment. Application: To apply for this position, please send an email with your Curriculum Vitae, a document with your academic results (if possible), and a couple of lines describing your motivation to pursue a PhD to mihai[dot]andries[at]imt-atlantique[dot]fr before 16 May 2023. Additional Information : Application deadline : 16 May 2023 Start date : Fall 2023 Contract duration: 36 months Localisation - Location : Brest (France) and Lisbon (Portugal) Contact(s) : Mihai ANDRIES (mihai[dot]andries[at]imt-atlantique.fr) Plinio Moreno (plinio[at]isr.tecnico.ulisboa.pt)
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| 6-42 | (2023-05-11) PhD position @ ISIR and IRCAM Paris France
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| 6-43 | (2023-05-11) PhD-student position in artificial intelligence, human-robot interaction @ KTH, Stockholm, Sweden We are looking for a PhD student who is interested in Artificial Intelligence, Machine Learning, Natural Language Processing and Human-Robot Interaction. The doctoral student will work in a newly funded project at the Department of Speech, Music and Hearing within the School of Electrical Engineering and Computer Science at KTH. The project is financed by the Swedish AI-program WASP (Wallenberg AI, Autonomous Systems and Software Program) which offers a graduate school with research visits, partner universities, and visiting lecturers.
The newly started project is titled 'Anticipatory Control in Conversational Human-Robot Interaction'. The aim of the project is to use self-supervised learning to develop generic language models for human-robot interaction and explore how such models can be used in real time to predict and anticipate human behavior and thereby improve the interaction. Whereas traditional language models in NLP (such as BERT, GPT) have focused on written language, we want to model multi-modal conversation, where aspects such as engagement, turn-taking, and incremental processing are of importance. This means that the models will have to process both text, audio and video, including aspects such as how the human users move and their facial expressions. In collaboration with industry and other projects, we will then explore how such models can be used for social robotic applications. Another important focus will be on model analysis and visualization.
For more information about the position, see https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:623390/where:4/
If you have any questions, don’t hesitate to contact Prof. Gabriel Skantze (skantze@kth.se)
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| 6-44 | (2023-05-15) Post-doctoral and engineer positions@ LORIA-INRIA, Nancy, France Automatic speech recognition for non-natives speakers in a noisy environment
Post-doctoral and engineer positions
Starting date: July-September 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
Emmanuel Vincent, Senion Research Scientist & Head of Science, INRIA Multispeech Team, emmanuel.vincent@inria.fr
http://members.loria.fr/evincent/Cons: the application must meet the requirements of the French Directorate General of Armament (Direction générale de l'armement, DGA).
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:
- 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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| 6-45 | (2023-05-18) PhD position @ GIPSA Lab, Grenoble, France Nous proposons une offre de thèse en acoustique-aérodynamique-mécatronique de la parole, dans le cadre du projet ANR AVATARS (« Artificial Voice production: control of bio-inspired port-HAmilToniAn numeRical and mechatronic modelS », 2023-2027). Le sujet porte sur la 'Caractérisation du comportement vocal humain dans la parole et dans le chant sur banc mécatronique robotisé. Application au développement de plis vocaux biomimétiques.' Pour plus d'information et pour candidater :
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| 6-46 | (2023-05-20) Poste d' enseignant chercheur, Grenoble, France Nous recherchons pour l'année 2023-2024 une personne pour un CDD 50% enseignement et
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| 6-47 | (2023-05-22) PhD position @Lille and Grenoble, France Nous recherchons un·e candidat·e pour une thèse sur la modélisation computationnelle du
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| 6-48 | (2023-05-22) PhD Causal Machine Learning Applied to NLP and the Study of Large Language Models, Grenoble, France Job Offer: PhD Causal Machine Learning Applied to NLP and the Study of Large Language
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| 6-49 | (2023-05-27) PhD position@ EECS-KTH, Stockholm, Sweden The School of Electrical Engineering and Computer Science (EECS) at the KTH Royal Institute of Technology has an open Ph.D position in Social Robotics at the division of Robotics, Perception and Learning (RPL).
ABOUT KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key center of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
PROJECT DESCRIPTION
This project addresses the challenge of how to enable robots to learn in a scalable and cost-efficient manner by gradually acquiring new knowledge from non-expert, semi-situated teachers. To achieve this, computational methods will be developed for robots to query the semi-situated teachers (e.g. crowd workers) and incorporate the newly acquired knowledge into their existing decision-making to further use in situ. This project is funded by the Swedish Foundation for Strategic Research.
The starting date for the positions is flexible, but preferably during the fall of 2023.
QUALIFICATIONS
The candidate must have a degree in Computer Science or related fields. Documented written and spoken English and programming skills are required. Experience with robotics, human-robot interaction, human-computer interaction, multimodal interaction and machine learning are important.
HOW TO APPLY
The application should include:
1. Curriculum vitae.
2. Transcripts from University/College.
3. Brief description of why the applicant wishes to become a doctoral student.
The application documents should be uploaded using the KTH's recruitment system. More information here:
The application deadline is ** June 2, 2023 **
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| 6-50 | (2023-05-28) Ph.D. position: Automatic speech recognition for non-natives speakers in a noisy environment, LORIA-INRIA, Nancy, France Automatic speech recognition for non-natives speakers in a noisy environment
Ph.D. position
Starting date: September-October 2023
Duration: 36 months
Supervisors: Irina Illina, Associate Professor, HDR Lorraine University LORIA-INRIA Multispeech Team, illina@loria.fr
Emmanuel Vincent, Senior Research Scientist & Head of Science, INRIA Multispeech Team, emmanuel.vincent@inria.fr
http://members.loria.fr/evincent/Cons: the application must meet the requirements of the French Directorate General of Armament (Direction générale de l'armement, DGA). 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, the 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 in the models used by ASR.
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:
- Master's 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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| 6-51 | (2023-05-30) PhD position in NLP, @ Jozef International postgraduate School (Slovenia) and La Rochelle University (France).
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| 6-52 | (2023-06-01) PhD position @ Computer Science Lab in Bordeaux, France (LaBRI) and the LORIA (Nancy, France) In the framework of the PEPR Santé numérique “Autonom-Health” project (Health, behaviors and autonomous digital technologies), the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) and the LORIA (Nancy, France) are looking for candidates for a fully funded PhD position (36 months). The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email.
The missions that will be addressed by the retained candidates are among these tasks, according to the profile of the candidate:
- Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs) - Collection of patient/doctor interactions during clinical interviews - ASR-related tasks - Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients, - Adaptation of the ASR system to clinical interviews domain, - Automatic phonetic transcription / alignment using end2end architectures - Adapting ASR transcripts to be used with semantic analysis tools developed at LORIA - Speech analysis tasks - Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases. The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the LORIA teams : « Multispeech » (contact: Emmanuel Vincent emmanuel.vincent@inria.fr) and/or the « Sémagramme » (contact: Maxime Amblard maxime.amblard@loria.fr). Gross salary: approx. 2044 €/month Starting date: October 2023
Required qualifications: Master in Signal processing / speech analysis / computer science Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, excellent command of French (interactions with French patients and clinicians), good level of scientific English. Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design. Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts. Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV, cover letter (describing your personal qualifications, research interests and motivation for applying), contact information of two referees and academic certificates (Master, Bachelor certificates). —— Abstract of the « Autonom-Health » project: Western populations face an increase of longevity which mechanically increases the number of chronic disease patients to manage. Current healthcare strategies will not allow to maintain a high level of care with a controlled cost in the future and E health can optimize the management and costs of our health care systems. Healthy behaviors contribute to prevention and optimization of chronic diseases management, but their implementation is still a major challenge. Digital technologies could help their implementation through numeric behavioral medicine programs to be developed in complement (and not substitution) to the existing care in order to focus human interventions on the most severe cases demanding medical interventions. However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
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| 6-53 | (2023-06-02) Open faculty position at KU Leuven, Belgium: junior professor in Synergistic Processing of Multisensory Data for Audio-Visual Understanding Open faculty position at KU Leuven, Belgium: junior professor in Synergistic Processing
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| 6-54 | (2023-06-04) PhD in ML/NLP @ Dauphine Université PSL, Paris and Université Grenoble Alpes, France PhD in ML/NLP – Fairness and self-supervised learning for speech processing Salary: ~2000€ gross/month (social security included) Mission: research oriented (teaching possible but not mandatory)
Keywords: speech processing, fairness, bias, self-supervised learning, evaluation metrics
CONTEXT This thesis is in the context of the ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies). Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks.
PROJECT OBJECTIVES Speech technologies are widely used in our daily life and are expanding the scope of our action, with decision-making systems, including in critical areas such as health or legal aspects. In these societal applications, the question of the use of these tools raises the issue of the possible discrimination of people according to criteria for which society requires equal treatment, such as gender, origin, religion or disability... Recently, the machine learning community has been confronted with the need to work on the possible biases of algorithms, and many works have shown that the search for the best performance is not the only goal to pursue [1]. For instance, recent evaluations of ASR systems have shown that performances can vary according to the gender but these variations depend both on data used for learning and on models [2]. Therefore such systems are increasingly scrutinized for being biased while trustworthy speech technologies definitely represents a crucial expectation.
- First make a survey on the many definitions of robustness, fairness and bias with the aim of coming up with definitions and metrics fit for speech SSL models - Then gather speech datasets with high amount of well-described metadata - Setup an evaluation protocol for SSL models and analyzing the results.
SKILLS
SCIENTIFIC ENVIRONMENT The PhD position will be co-supervised by Alexandre Allauzen (Dauphine Université PSL, Paris) and Solange Rossato and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, two other PhD positions are open in this project. The students, along with the partners will closely collaborate. For instance, specific SSL models along with evaluation criteria will be developed by the other PhD students. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited and the partners from LIA, LIG and Dauphine Université PSL, Paris. The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and Dauphine Université PSL. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments.
INSTRUCTIONS FOR APPLYING Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Alexandre Allauzen (alexandre.allauzen@espci.psl.eu), Solange Rossato (Solange.Rossato@imag.fr) and François Portet (francois.Portet@imag.fr). We celebrate diversity and are committed to creating an inclusive environment for all employees.
REFERENCES: [1] Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J. & Tuennerman, E. “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans. Frontiers in Artificial Intelligence 4. issn: 2624-8212. https://www.frontiersin.org/article/10.3389/frai.2021.725911 (2021). [2] Garnerin, M., Rossato, S. & Besacier, L. Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech in Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021), 86–92.
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| 6-55 | (2023-06-06) Postdoc in recognition and translation @LABRI, Bordeaux, France In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr 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).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr 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).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr 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).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr 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).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
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| 6-56 | (2023-06-02) Transcriptors for ELDA Paris France ELDA (Evaluations and Language resources Disctribution Agency) looks for full/part time transcriptors for transcription of phone calls in the financial domain. Location: ELDA (Paris-France) Latest starting date: July 2023 Languages and mission details
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| 6-57 | (2023-06-08) Postdoc @ ENS,Paris, France DRhyaDS A new framework for understanding the Dynamic Rhythms and Decoding of Speech Job Title - Postdoctoral Researcher Disciplines and Areas of Research - Speech science, Psycholinguistics, Psychoacoustics Contract Duration - 1 Year Research Overview: The DRhyaDS project aims to develop a new framework for understanding the dynamic rhythms and decoding of speech. It focuses on exploring the temporal properties of speech and their contribution to speech perception. The project challenges the conventional view that speech rhythm perception relies on a one-to-one association between specific modulation frequencies in the speech signal and linguistic units. One of the key objectives of the project is to investigate the impact of language-specific temporal characteristics on speech dynamics. The project team will analyze two corpora of semi-spontaneous speech data from French and German, representing syllable-timed and stress-timed languages, respectively. Various acoustic analyses will be conducted on these speech corpora to explore the variability of slow temporal modulations in speech at an individual level. This comprehensive acoustic exploration will involve extracting and analyzing prosody, spectral properties, temporal dynamics, and rhythmic patterns. By examining these acoustic parameters, the project aims to uncover intricate details about the structure and variation of speech signals across languages and speakers, contributing to a more nuanced understanding of the dynamic nature of spoken language and its role in human communication. Environment: The selected candidate will be an integral part of an international research team and will work in a collaborative and stimulating lab environment. The project brings together a FrancoGerman team of experts in linguistics, psychoacoustics and cognitive neuroscience, led by Dr. Léo Varnet (CNRS, ENS Paris) and Dr. Alessandro Tavano (Max Planck Institute, Goethe University Frankfurt). The successful candidate will work under the supervision of Dr Léo Varnet, at the Laboratoire des Systèmes Perceptifs (ENS Paris). Job description: This is a one-year postdoctoral contract position, offering a net salary in accordance with French legislation (~2500€/month + social and medical benefits). Women and minorities are strongly encouraged to apply. The successful candidate will participate in research activities, collaborate with team members, and contribute to scientific publications and communications. Additionally, they will have the autonomy to suggest and implement their own analysis techniques and approaches. Their responsibilities will include: - Taking a lead role in collecting a comprehensive corpus of French speech data, adhering to a rigorous data collection protocol - Collaborating closely with the German team to leverage the existing German speech corpus for comparative analysis and cross-linguistic investigations - Conducting in-depth acoustic analysis of the corpora, employing advanced techniques to investigate the variability and dynamics of slow temporal modulations in speech - Actively participating in team meetings, workshops, and conferences to present research progress, exchange ideas, and contribute to the intellectual growth of the project - Engaging in science outreach activities to promote the project's research outcomes and facilitate public understanding of speech perception and language processing. Qualifications: - A recently obtained PhD in a relevant field (e.g., linguistics, psychology, neuroscience, computational sciences) - Strong expertise in linguistics, speech perception, acoustic analysis, and statistical methods - Proficiency in programming languages commonly used in speech research. Knowledge of MATLAB would be particularly valuable for data processing and analysis within the project. - Strong written and verbal communication skills in English. Candidates with proficiency in French and/or German language skills would be particularly appreciated, as it would enable a deeper understanding of the linguistic characteristics of the respective corpora. Application process To apply for this position, please submit a CV and a cover letter (in French or English) along with the names and contact information of 2 referees to Léo Varnet (leo.varnet@cnrs.fr). The application deadline is 31th July 2023. Interviews will be conducted in September. The ideal start date is October-November 2023, with some flexibility allowed. Feel free to get in touch informally to discuss this position
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| 6-58 | (2023-06-16) PhD funded position@ INRIA France Inria is opening a fully funded PhD position on multimodal speech
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| 6-59 | (2023-06-16) Post doc @ IMAG, Grenoble, France Call for postdoc applications in Natural Language Processing for the automatic detection of gender stereotypes in the French media (Grenoble Alps University, France) Starting date: flexible, November 30, 2023, at the latest Duration: full-time position for 12 months Salary: according to experience (up to 4142€/ month) Application Deadline: Open until filled Location: The position will be based in Grenoble, France. This is not a remote work. Keywords: natural language processing, gender stereotypes bias, corpus analysis, language models, transfer learning, deep learning *Context* The University of Grenoble Alps (UGA) has an open position for a highly motivated postdoc researcher to joint the multidisciplinary GenderedNews project. Natural Language Processing models trained on large amount of on-line content, have quickly opened new perspectives to process on-line large amount of on-line content for measuring gender bias in a daily basis (see our project https://gendered-news.imag.fr/ ). Regarding research on stereotypes, most recent works have studied Language Models (LM) from a stereotype perspective by providing specific corpora such as StereoSet (Nadeem et al., 2020) or CrowS-Pairs (Nangia et al. 2020). However, these studies are focusing on the quantifying of bias in the LM predictions rather than bias in the original data (Choenni et al., 2021). Furthermore, most of these studies ignore named entities (Deshpande et al., 2022) which account for an important part of the referents and speakers in news. In this project, we intend to build corpora, methods and NLP tools to qualify the differences between the language used to describe groups of people in French news. *Main Tasks* The successful postdoc will be responsible for day-to-day running of the research project, under the supervision of François Portet (Prof UGA at LIG) and Gilles Bastin (prof UGA at PACTE). Regular meetings will take place every two weeks. - Defining the dimensions of stereotypes to be investigated and the possible metrics that can be processed from a machine learning perspective. - Exploring, managing and curating news corpora in French for stereotypes investigation, with a view to making them widely available to the community to favor reproducible research and comparison. - Studying and developing new computational models to process large number of texts to reveal stereotype bias in news. Make use of pretrained models for the task. - Evaluate the methods on curated focused corpus and apply it to the unseen real longitudinal corpus and analyze the results with the team. - Preparing articles for submission to peer-reviewed conferences and journals. - Organizing progress meetings and liaising between members of the team. The hired person will interact with PhD students, interns and researchers being part of the GenderedNews project. According to his/her background his/her own interests and in accordance with the project's objective, the hired person will have the possibility to orient the research in different directions. *Scientific Environment* The recruited person will be hosted within the GETALP teams of the LIG laboratory (https://lig-getalp.imag.fr/), which offers a dynamic, international, and stimulating environment for conducting high-level multidisciplinary research. The person will have access to large datasets of French news, GPU servers, to support for missions as well as to the scientific activities of the labs. The team is housed in a modern building (IMAG) located in a 175-hectare landscaped campus that was ranked as the eighth most beautiful campus in Europe by the Times Higher Education magazine in 2018. The person will also closely work with Gilles Bastin (PACTE, a Sociology lab in Grenoble) and Ange Richard (PhD at LIG and PACTE). The project also includes an informal collaboration with 'Prenons la une' (https://prenonslaune.fr/) a journalists’ association which promotes a fair representation of women in the media. *Requirements* The candidate must have a PhD degree in Natural Language Processing or computer science or in the process of acquiring it. The successful candidate should have - Good knowledge of Natural Language Processing - Experience in corpus collection/formatting and manipulation. - Good programming skills in Python - Publication record in a close field of research - Willing to work in multidisciplinary and international teams - Good communication skills - Good mastering of French is required *Instructions for applying* Applications will be considered on the fly and must be addressed to François Portet (Francois.Portet@imag.fr). It is therefore advisable to apply as soon as possible. The application file should contain - Curriculum vitae - References for potential letter(s) of recommendation - One-page summary of research background and interests for the position - Publications demonstrating expertise in the aforementioned areas - Pre-defense reports and defense minutes; or summary of the thesis with the date of defense for those currently in doctoral studies *References* Deshpande et al. (2022). StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes. arXiv preprint arXiv:2205.14036. Choenni et al. (2021). Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? arXiv preprint arXiv:2109.10052. Nadeem et al. (2020) StereoSet: Measuring stereotypical bias in pretrained language models. ArXiv. Nangia et al. (2020) CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In EMNLP2020.
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| 6-60 | (2023-06-16) 6 Post-doc positions in 'Education and AI in the 21st century. Technology-enabled innovations in subject-specific teaching settings (PostdocTEIFUN)' , Tübingen and Stuttgart, Germany
the new Postdoc-Kolleg 'Education and AI in the 21st century. Technology-enabled innovations in subject-specific teaching settings (PostdocTEIFUN)' of the Tübingen School of Education (TüSE) and Professional School of Education Stuttgart-Ludwigsburg (PSE) will start in 2024.
It is offering six Post-doc positions funded for six years (100% TVL E14) to conduct interdisciplinary research in the field of education and AI.
The official announcement can be found here:
Among the various potential research fields, the following could be of interest here:
'Development of didactically informed Intelligent Language Tutoring approaches targeting spoken language learning for the English classroom. Potential directions include but are not limited to the realization of perceptual training, input enhancement, automatic corrective feedback, or prosodic complexity analysis.'
Interested? Please contact us at: Sabine Zerbian, sabine.zerbian@ifla.uni-stuttgart.de Detmar Meurers, detmar.meurers@uni-tuebingen.de
Note that there is a tight deadline for applications: 30.06.2023
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| 6-61 | (2023-06-17) PhD @ NTNU, Trondheim, Norway The announcement is here:
Deadline is 2023-07-31.
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| 6-62 | (2023-06-20) PhD position @ University of Applied Sciences, Hochschule Hannover, Germany
In a joint research project *VidQA*, our collaboration partner from University of Applied Sciences (Prof. Christian Wartena, HsH Hochschule Hannover) offers a full position (three years, as soon as possible) for a PhD student:
*VidQA* is a joint research project of the Institute for Applied Data Science Hannover (DATA|H) of the HsH, the Research Center L3S of the Leibniz University and the TIB – Leibniz Information Centre for Science and Technology. The goal of the project is the development and evaluation of new methods for semi-automatic generation of questions and answers for learning videos. Here, we pursue several research questions, such as, among others, the aspect of multimodality of video-based learning media, the generation of distractors ('wrong answers') for multiple-choice questions, and the automatic evaluation of answers for open-ended question formats.
*What you can expect*: - Develop and implement procedures for generating (multiple choice) questions, answers, and distractors. - Development and implementation of procedures for scoring free text answers - Collaboration in the development and evaluation of a system for examing comprehension of learning videos - Publication of research and development results at conferences and in professional journals - Participation in the organization the project
*Requirements* Research Field: Computer science Education Level: Master Degree or equivalent Skills/Qualifications - Master's degree (or equivalent) in computer science or computational linguistics - In-depth knowledge in the field of artificial intelligence and machine learning - Proven knowledge in Natural Language Processing (NLP) - Gender and diversity skills
Languages: ENGLISH Level: Excellent
Languages: GERMAN Level: Good
Website for additional job details: https://karriere.hs-hannover.de/bewerbung/beschreibung-900000104-10057.html
Where to apply: https://karriere.hs-hannover.de/bewerbung/beschreibung-900000104-10057.html *First Contact* ************************************ Prof. Dr. Christian Wartena University of Applied Sciences (HsH Hochschule Hannover) Institute for Applied Data Science Hannover (DATA|H) E-Mail: christian.wartena@hs-hannover.de
Postal address City: Hannover Website: https://www.hs-hannover.de/forschung/forschungsaktivitaeten/forschungscluster/smart-data-analytics Street: Expo Plaza 12 Postal Code: 30539
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| 6-63 | (2023-06-26) These CIFRE pleinement financée, IMAG et Eloquant, Grenoble, France
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| 6-64 | (2023-06-27) Research Associate in Integrated Multitask Neural Speech Labelling, University of Sheffield, UK Deadline July 13th, 2023
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| 6-65 | (2023-07-01) Offre de thèse en 'Apprentissage profond pour l'identification du locuteur et séparation de la parole', CNRS Pour plus d'informations voir :
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