3.1.1 Integrating domain knowledge

State-of-the-art methods in speech and audio are based on neural networks trained for the targeted task. This paradigm faces major limitations: lack of interpretability and of guarantees, large data requirements, and inability to generalize to unseen classes or tasks. We research deep generative models as a way to learn task-agnostic probabilistic models of audio signals and design inference methods to combine and reuse them for a variety of tasks. We pursue our investigation of hybrid methods that combine the representational power of deep learning with statistical signal processing expertise by leveraging recent optimization techniques for non-convex, non-linear inverse problems. We also explore the integration of deep learning and symbolic reasoning to increase the generalization ability of deep models and to empower researchers/engineers to improve them.

3.1.2 Learning from little/no labeled data

While fully labeled data are costly, unlabeled data are cheap but provide intrinsically less information. Weakly supervised learning based on not-so-expensive incomplete and/or noisy labels is a promising middle ground. This entails modeling label noise and leveraging it for unbiased training. Models may depend on the labeler, the spoken context (voice command), or the temporal structure (ambient sound analysis). We also study transfer learning to adapt an expressive (audiovisual) speech synthesizer trained on a given speaker to another speaker for which only neutral voice data has been collected.

3.1.3 Preserving privacy

Some voice technology companies process users' voices in the cloud and store them for training purposes, which raises privacy concerns. We aim to hide speaker identity and (some) speaker states and traits from the speech signal, and evaluate the resulting automatic speech/speaker recognition accuracy and subjective quality/intelligibility/identifiability, possibly after removing private words from the training data. We also explore semi-decentralized learning methods for model personalization, and seek to obtain statistical guarantees.

3.2.1 Articulatory modeling

Articulatory speech synthesis relied on 2D and 3D modeling of the dynamics of the vocal tract from real-time MRI data. The prediction of glottis opening is also considered so as to produce better quality acoustic events for consonants. The coarticulation model developed to handle the animation of the visible articulators will be extended to control the face and the tongue. This helps characterize links between the vocal tract and the face, and illustrate inner mouth articulation to learners. The suspension of articulatory movements in stuttering speech is also studied.

3.2.2 Multimodal expressive speech

The dynamic realism of the animation of the talking head, which has a direct impact on audiovisual intelligibility, continues to be our goal. Both the animation of the lower part of the face relating to speech and of the upper part relating to the facial expression are considered, and development continues towards a multilingual talking head. We investigate further the modeling of expressivity both for audio-only and for audiovisual speech synthesis. We also evaluate the benefit of the talking head in various use cases, including children with language and learning disabilities or deaf people.

3.2.3 Categorization of sounds and prosody

Reading and speaking are basic skills that need to be mastered. Further analysis of schooling experience will allow a better understanding of reading acquisition, especially for children with some language impairment. With respect to L1/L2 language interference1, a special focus is set on the impact of L2 prosody on segmental realizations. Prosody is also considered for its implication on the structuration of speech communication, including on discourse particles. Moreover, we experiment the usage of speech technologies for computer assisted language learning in middle and high schools, and, hopefully, also for helping children learning to read.

3.3.1 Acoustic environment analysis

Audio scene analysis is key to characterize the environment in which spoken communication may take place. We investigate audio event detection methods that exploit both strongly/weakly labeled and unlabeled data, operate in real-world conditions, can discover novel events, and provide a semantic interpretation. We keep working on source localization in the presence of nearby acoustic reflectors. We also pursue our effort at the interface of room acoustics to blindly estimate room properties and develop acoustics-aware signal processing methods. Beyond spoken communication, this has many applications to surveillance, robot audition, building acoustics, and augmented reality.

3.3.2 Speech enhancement and noise robustness

We pursue speech enhancement methods targeting several distortions (echo, reverberation, noise, overlapping speech) for both speech and speaker recognition applications, and extend them to ad-hoc arrays made of the microphones available in our daily life using multi-view learning. We also continue to explore statistical signal models beyond the usual zero-mean complex Gaussian model in the time-frequency domain, e.g., deep generative models of the signal phase. Robust acoustic modeling will be achieved by learning domain-invariant representations or performing unsupervised domain adaptation on the one hand, and by extending our uncertainty-aware approach to more advanced (e.g., nongaussian) uncertainty models and accounting for the additional uncertainty due to short utterances on the other hand, with application to speaker and language recognition “in the wild”.

3.3.3 Linguistic and semantic processing

We seek to address robust speech recognition by exploiting word/sentence embeddings carrying semantic information and combining them with acoustical uncertainty to rescore the recognizer outputs. We also combine semantic content analysis with text obfuscation models (similar to the label noise models to be investigated for weakly supervised training of speech recognition) for the task of detecting and classifying (hateful, aggressive, insulting, ironic, neutral, etc.) hate speech in social media.

7.1.1 COMPRISE Voice Transformer

• Name: COMPRISE Voice Transformer
• Keywords: Speech, Privacy
• Functional Description: COMPRISE Voice Transformer is an open source tool that increases the privacy of users of voice interfaces by converting their voice into another person’s voice without modifying the spoken message. It ensures that any information extracted from the transformed voice can hardly be traced back to the original speaker, as validated through state-of-the-art biometric protocols, and it preserves the phonetic information required for human labelling and training of speech-to-text models.
• Release Contributions: This version gives access to the 2 generations of tools that have been used to transform the voice, as part of the COMPRISE project (https://www.compriseh2020.eu/). The first one is a python library that implements 2 basic voice conversion methods, both using VLTN. The second one implements an anonymization method using x-vectors and neural waveform models.
• URL: https://gitlab.inria.fr/comprise/voice_transformation
• Contact: Marc Tommasi
• Participants: Nathalie Vauquier, Brij Mohan Lal Srivastava, Marc Tommasi, Emmanuel Vincent, Md Sahidullah

7.1.2 COMPRISE Weakly Supervised STT

• Name: COMPRISE Weakly Supervised Speech-to-Text
• Keywords: Speech recognition, Language model, Acoustic Model
• Functional Description: COMPRISE Weakly Supervised Speech-to-Text provides two main components for training Speech-to-Text (STT) models. These two components represent the two main approaches proposed in the COMPRISE project, namely (a) semi-supervised training driven by error predictions and (b) weakly supervised training based on utterance level weak labels. These two approaches can be used independently or together. The implementation builds on the Kaldi toolkit. It mainly focuses on obtaining reliable transcriptions of un-transcribed speech data which can be used for training both STT acoustic model (AM) and language model (LM). AM can be any type, although we choose the state-of-the-art TDNN Chain AM in our examples. Statistical n-gram LMs are chosen to support limited data scenarios.
• URL: https://gitlab.inria.fr/comprise/speech-to-text-weakly-supervised-learning
• Authors: Imran Sheikh, Emmanuel Vincent, Irina Illina
• Contact: Emmanuel Vincent

7.1.3 Kaldi-web

• Name: Kaldi-web
• Keyword: Speech recognition
• Functional Description: Today, developers willing to implement a voice interface must either rely on proprietary software or become experts in speech recognition. Conversely, researchers in speech recognition wishing to demonstrate their results need to be familiar with other technologies (e.g., graphical user interfaces). Kaldi-web is an open-source, cross-platform tool which bridges this gap by providing a user interface built around the online decoder of the Kaldi toolkit. Additionally, because we compile Kaldi to Web Assembly, speech recognition is performed directly in web browsers. This addresses privacy issues as no data is transmitted to the network for speech recognition.
• URL: https://gitlab.inria.fr/kaldi.web/
• Contact: Denis Jouvet
• Participants: Mathieu Hu, Laurent Pierron, Denis Jouvet, Emmanuel Vincent

7.1.4 Asteroid

• Name: Asteroid: The PyTorch-based audio source separation toolkit for researchers.
• Keywords: Source Separation, Deep learning
• Functional Description: Asteroid is an open-source toolkit made to design, train, evaluate, use and share neural network based audio source separation and speech enhancement models. Inspired by the most successful neural source separation systems, Asteroid provides all neural building blocks required to build such a system. To improve reproducibility, Kaldi-style recipes on common audio source separation datasets are also provided. Experimental results obtained with Asteroid’s recipes show that our implementations are at least on par with most results reported in reference papers.
• URL: https://github.com/asteroid-team/asteroid
• Contact: Antoine Deleforge
• Participants: Manuel Pariente, Mathieu Hu, Joris Cosentino, Sunit Sivasankaran, Mauricio Michel Olvera Zambrano, Fabian Robert Stoter

7.1.5 DNNuep

• Name: DNN uncertainty estimation and propagation
• Keyword: Speech recognition
• Functional Description: From a noisy signal and its noiseless version, the system estimates the uncertainty and propagates it in an automatic speech recognition system.
• News of the Year: Development of version 1.0 of the software. Propagation of uncertainty with two methods and implementation in Kaldi. Performance evaluation on a noisy speech corpus.
• Contact: Irina Illina
• Participants: Irina Illina, Dominique Fohr, Ismaël Bada

7.1.6 DNNsem

• Keywords: Speech recognition, Semantic
• Functional Description: DNNsem is a software for rescoring the N-best list of hypotheses of an automatic speech recognition (ASR) system. It is useful in the situation when the training and testing conditions differ due to noise or other acoustic distortions. To improve performance in such conditions, DNNsem accounts for long-term semantic relations. It takes as inputs the list of N-best ASR hypotheses and the corresponding acoustic scores, and it outputs a reranked list by favoring words that better correspond to the semantic context of the sentence. To do so, it uses two continuous word representations: word2vec or FastText.
• News of the Year: Development of version 1.0 of the software. Implementation of two methods, based on static word embeddings (word2vec) or dynamic word embeddings (BERT). Performance evaluation on a noisy speech corpus.
• Contact: Irina Illina
• Participants: Irina Illina, Dominique Fohr

7.1.7 Web-based Pronunciation Learning Application

• Keywords: Pronunciation training, Talking head, Second language learning
• Functional Description: This web-based application is dedicated to foreign language pronunciation learning (current version was developed for the German language). It is intended for high school and middle school students. There are two types of exercises that are integrated in this application. (1) Flashcards: Cards are presented, then a virtual teacher (a 3D talking head) pronounces the words and sentences corresponding to these cards. Students can practice and make an evaluation of their word comprehension. (2) Speech recognition. The application displays a list of words/phrases that the student pronounces and the system gives feedback on the quality of the pronunciation. This application is composed of two modules: one for students (described above) and one for teachers, allowing them to create lessons, and to follow the results and progress of student evaluations.
• Contact: Slim Ouni
• Participants: Thomas Girod, Leon Rohrbacher, Slim Ouni, Denis Jouvet

7.1.8 Grapheme-phoneme aligner

• Keywords: Grapheme-to-phoneme converter, Grapheme-phoneme alignment
• Functional Description: This software processes French words or sentences to determine their pronunciation, and to provide the association between letters and sounds. It calls SOJA to preprocess the text, and LORIA-PHON to determine the pronunciation of the words. It then aligns, through a set of rules, the letters of the text with the phonemes of the predicted pronunciation.
• Contact: Vincent Colotte
• Participants: Vincent Colotte, Louis Delebecque, Denis Jouvet

8.1.1 Integrating domain knowledge

8.1.2 Learning from little/no labeled data

8.1.3 Preserving privacy

Speech signals involve a lot of private information. With a few minutes of data, the speaker identity can be modeled for malicious purposes like voice cloning, spoofing, etc. To reduce this risk, we investigated speaker anonymization strategies based on voice conversion. In contrast to prior evaluations, we argue that different types of attackers can be defined depending on the extent of their knowledge. We compared three simple conversion methods in three attack scenarios, and showed that these methods fail to protect against an attacker that has extensive knowledge of the type of conversion and how it has been applied, but may provide some protection against less knowledgeable attackers 60. We then developed a more advanced conversion method and explored several design choices for the distance metric between the source and target speakers, the region of x-vector space where the target speaker is picked, and gender selection to find the optimal combination of design choices in terms of privacy and utility 59. The resulting software served as a baseline for the 1st Voice Privacy Challenge 61. We have investiagted the modification of the fundamental frequency to improve consistency with the selected target x-speaker 89, 23. We also conducted a comparative study of speech anonymization metrics from a theoretical and experimental point of view 48.

8.2.1 Articulatory modeling

8.2.2 Multimodal expressive speech

8.2.3 Categorization of sounds and prosody

8.3.1 Acoustic environment analysis

Sound event detection is the task of finding what sound event occurred in a recording and when. As it is prohibitive to get a large dataset with so-called strong labeled soundcases (i.e., with onset and offset timestamps), one alternative is to rely on so-called weakly labeled soundscapes (i.e., without timestamps) that are considerably cheaper to obtain. We explored the limitations introduced by relying only on such weak labels 65. Another alternative would be to generate synthetic soundscapes for which strong annotations are then cheap to obtain but a the cost of possible domain mismatch with recorded evaluation data. We studied the impact of training a sound event detection system using a heterogeneous dataset (including both recorded and synthetic soundscapes) and different label granularity (strong, weak) 64. An additional problem when working with real, complex soundscapes is that they can involve multiple overlapping sound events. We proposed to adapt a standard sound separation algorithm and used it as a front-end to sound event detection on such complex soundscapes 50.

Pursuing our involvement in the community on ambient sound recognition, we co-organized a task on sound event detection and separation as part of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge 64, 66 and published a detailed analysis of the submissions to the previous iteration of this task in 2019 54. In 2020, the task still focused on the problem of learning from audio segments that are either weakly labeled or unlabeled, targeting domestic applications. We also proposed to investigate possible improvement obtained with a sound separation front-end used jointly with sound event detection in the case of complex soundscapes 66. This additional aspect attracted researchers from the sound separation community, including some researchers from the team (not involved in the task organization) who proposed two different approaches to combine sound event detection and sound separation 25, 24.

We also pursued our work in estimating acoustical properties of environments from recorded audio, e.g., room shape, reverberation time or absorption coefficients. Much of the information is contained in early acoustic echoes, stemming from the sound interaction with reflective materials in the room. A new anaytical method for blind early acoustic echo retrieval based on the framework of continuous dictionary learning was proposed in 29. A new approach for mean absorption coefficient estimation from impulse reponses using virtually-supervised learning was presented in 22.

8.3.2 Speech enhancement and noise robustness

8.3.3 Linguistic and semantic processing

9.1.1 Dassault and Thalès - Man Machine Teaming Initiative

• Company: Dassault and Thalès (France)
• Duration: Apr 2019 - Sept 2020
• Participants: Irène Illina, Dominique Fohr, Ismaël Bada, Stéphane Level
• Abstract: The primary goal of the project is to develop a new approach that allows coupling speech enhancement with semantic analysis for improving speech recognition robustness.

9.2.1 Invoxia

• Company: Invoxia SAS (France)
• Duration: Mar 2017 – Apr 2020
• Participants: Guillaume Carbajal, Romain Serizel, Emmanuel Vincent
• Abstract: This CIFRE contract funded the PhD thesis of Guillaume Carbajal. We designed a unified deep learning based speech enhancement system that integrates all steps in the current speech enhancement chain (acoustic echo cancellation and suppression, dereverberation, and denoising) for improved hands-free voice communication.

9.2.2 Ministère des Armées

• Company: Ministère des Armées (France)
• Duration: Sep 2018 – Aug 2021
• Participants: Raphaël Duroselle, Denis Jouvet, Irène Illina
• Abstract: This contract corresponds to the PhD thesis of Raphaël Duroselle on the application of deep learning techniques for domain adaptation in speech processing.

9.2.3 Facebook

• Company: Facebook AI Research (France)
• Duration: Nov 2018 – Nov 2021
• Participants: Adrien Dufraux, Emmanuel Vincent
• Abstract: This CIFRE contract funds the PhD thesis of Adrien Dufraux. Our goal is to explore cost-effective weakly supervised learning approaches, as an alternative to fully supervised or fully unsupervised learning for automatic speech recognition.

10.1.1 Inria international partners

10.2.1 FP7 & H2020 Projects

10.2.2 Collaborations in European programs, except FP7 and H2020

10.2.3 Collaborations with major European organizations

ANR ArtSpeech

• Title: Synthèse articulatoire phonétique
• Duration: Oct 2015 - Aug 2020
• Coordinator: Yves Laprie (LORIA, Nancy)
• Partners: LORIA (Nancy), Gipsa-Lab (Grenoble), IADI (Nancy), LPP (Paris)
• Participants: Ioannis Douros, Yves Laprie
• Abstract: The objective was to synthesize speech via the numerical simulation of the human speech production processes, i.e. the articulatory, aerodynamic and acoustic aspects. Articulatory data comes from MRI and EPGG acquisitions.

ANR JCJC KAMoulox

• Title: Kernel additive modelling for the unmixing of large audio archives
• Duration: Jan 2016 - May 2020
• Coordinator: Antoine Liutkus (Inria Zenith)
• Participants: Mathieu Fontaine
• Abstract: The objective was to develop theoretical and applied tools to embed audio denoising and separation tools in web-based audio archives. The applicative scenario was to deal with the notorious audio archive “Archives du CNRS — Musée de l'Homme”, gathering recordings dating back to the early 1900s.

PIA2 ISITE LUE

• Title: Lorraine Université d’Excellence
• Duration: Avr 2016 - Dec 2020
• Coordinator: Univ de Lorraine
• Participants: Ioannis Douros, Yves Laprie, Tulika Bose, Dominique Fohr, Irène Illina
• Abstract: LUE (Lorraine Université d’Excellence) was designed as an “engine” for the development of excellence, by stimulating an original dialogue between knowledge fields. The challenge number 6: “Knowledge engineering” has funded the PhD thesis of Ioannis Douros on articulatory modeling. The IMPACT initiative OLKI (Open Language and Knowledge for Citizens) funds the PhD thesis of Tulika Bose on the detection and classification of hate speech.

E-FRAN METAL

• Title: Modèles Et Traces au service de l’Apprentissage des Langues
• Duration: Oct 2016 - Sep 2020
• Coordinator: Anne Boyer (LORIA, Nancy)
• Partners: LORIA, Interpsy, LISEC, ESPE de Lorraine, D@NTE (Univ. Versailles Saint Quentin), Sailendra SAS, ITOP Education, Rectorat.
• Participants: Theo Biasutto-Lervat, Anne Bonneau, Vincent Colotte, Dominique Fohr, Elodie Gauthier, Thomas Girod, Denis Jouvet, Odile Mella, Slim Ouni, Leon Rohrbacher
• Abstract: METAL aims at improving the learning of languages (written and oral) through development of new tools and analysis of numeric traces associated with students' learning. MULTISPEECH is concerned by oral language learning aspects.

ANR VOCADOM

• Title: Robust voice command adapted to the user and to the context for ambient assisted living (http://vocadom.imag.fr/)
• Duration: Jan 2017 - Dec 2020
• Coordinator: CNRS - LIG (Grenoble)
• Partners: CNRS - LIG (Grenoble), Inria (Nancy), Univ. Lyon 2 - GREPS, THEORIS (Paris)
• Participants: Dominique Fohr, Md Sahidullah, Sunit Sivasankaran, Emmanuel Vincent
• Abstract: The goal is to design a robust voice control system for smart home applications. MULTISPEECH is responsible for wake-up word detection, overlapping speech separation, and speaker recognition.

ANR JCJC DiSCogs

• Title: Distant speech communication with heterogeneous unconstrained microphone arrays
• Duration: Sep 2018 – Mar 2022
• Coordinator: Romain Serizel (LORIA, Nancy)
• Participants: Louis Delebecque, Nicolas Furnon, Irène Illina, Romain Serizel, Emmanuel Vincent
• Collaborators: Télécom ParisTech, 7sensing
• Abstract: The objective is to solve fundamental sound processing issues in order to exploit the many devices equipped with microphones that populate our everyday life. The solution proposed is to apply deep learning approaches to recast the problem of synchronizing devices at the signal level as a multi-view learning problem.

ANR DEEP-PRIVACY

• Title: Distributed, Personalized, Privacy-Preserving Learning for Speech Processing
• Duration: Jan 2019 - Dec 2022
• Coordinator: Denis Jouvet (Inria, Nancy)
• Partners: MULTISPEECH (Inria Nancy), LIUM (Le Mans), MAGNET (Inria Lille), LIA (Avignon)
• Participants: Pierre Champion, Denis Jouvet, Emmanuel Vincent
• Abstract: The objective is to elaborate a speech transformation that hides the speaker identity for an easier sharing of speech data for training speech recognition models; and to investigate speaker adaptation and distributed training.

ANR ROBOVOX

• Title: Robust Vocal Identification for Mobile Security Robots
• Duration: Mar 2019 – Mar 2023
• Coordinator: Laboratoire d'informatique d'Avignon (LIA)
• Partners: Inria (Nancy), LIA (Avignon), A.I. Mergence (Paris)
• Participants: Antoine Deleforge, Sandipana Dowerah, Denis Jouvet, Romain Serizel
• Abstract: The aim is to improve speaker recognition robustness for a security robot in real environment. Several aspects will be particularly considered such as ambiant noise, reverberation and short speech utterances.

ANR LEAUDS

• Title: Learning to understand audio scenes
• Duration: Apr 2019 - Sep 2022
• Coordinator: Université de Rouen Normandie
• Partners: Université de Rouen Normandie, Inria (Nancy), Netatmo (Paris)
• Participants: Mauricio Michel Olvera Zambrano, Romain Serizel, Emmanuel Vincent, and Christophe Cerisara (CNRS - LORIA)
• Abstract: LEAUDS aims to make a leap towards developing machines that understand audio input through breakthroughs in the detection of thousands of audio events from little annotated data, the robustness to “out-of-the lab” conditions, and language-based description of audio scenes. MULTISPEECH is responsible for research on robustness and for bringing expertise on natural language generation.

Inria Project Lab HyAIAI

• Title: Hybrid Approaches for Interpretable AI
• Duration: Sep 2019 - Aug 2023
• Coordinator: Inria LACODAM (Rennes)
• Partners: Inria TAU (Saclay), SEQUEL, MAGNET (Lille), MULTISPEECH, ORPAILLEUR (Nancy)
• Participants: Irène Illina, Emmanuel Vincent, Georgios Zervakis
• Abstract: HyAIAI is about the design of novel, interpretable artificial intelligence methods based on hybrid approaches that combine state of the art numeric models with explainable symbolic models.

ANR BENEPHIDIRE

• Title: Stuttering: Neurology, Phonetics, Computer Science for Diagnosis and Rehabilitation
• Duration: Mar 2019 - Dec 2023
• Coordinator: Praxiling (Toulouse)
• Partners: Praxiling (Toulouse), LORIA (Nancy), INM (Toulouse), LiLPa (Strasbourg).
• Participants: Yves Laprie, Slim Ouni, Shakeel Ahmad Sheikh
• Abstract: This project brings together neurologists, speech-language pathologists, phoneticians, and computer scientists specializing in speech processing to investigate stuttering as a speech impairment and to develop techniques for diagnosis and rehabilitation.

ANR HAIKUS

• Title: Artificial Intelligence applied to augmented acoustic Scenes
• Duration: Dec 2019 - May 2023
• Coordinator: Ircam (Paris)
• Partners: Ircam (Paris), Inria (Nancy), IJLRA (Paris)
• Participants: Antoine Deleforge, Emmanuel Vincent
• Abstract: HAIKUS aims to achieve seamless integration of computer-generated immersive audio content into augmented reality (AR) systems. One of the main challenges is the rendering of virtual auditory objects in the presence of source movements, listener movements and/or changing acoustic conditions.

ANR Flash Open Science HARPOCRATES

• Title: Open data, tools and challenges for speaker anonymization
• Duration: Oct 2019 - Mar 2021
• Coordinator: Eurecom (Nice)
• Partners: Eurecom (Nice), Inria (Nancy), LIA (Avignon)
• Participants: Denis Jouvet, Md Sahidullah, Emmanuel Vincent
• Abstract: HARPOCRATES will form a working group that will collect and share the first open datasets and tools in the field of speech privacy, and launch the first open challenge on speech privacy, specifically on the topic of voice de-identification.

InriaHub Carnot Technologies Vocales

• Title: InriaHub Carnot Technologies Vocales
• Duration: Jan 2019 - Dec 2020
• Coordinator: Denis Jouvet
• Participants: Mathieu Hu, Denis Jouvet, Dominique Fohr, Vincent Colotte, Emmanuel Vincent, Romain Serizel
• Abstract: This project aims to adjust and finalize the speech synthesis and recognition modules developed for research purposes in the team, so that they can be used in interactive mode.

Action Exploratoire Inria Acoust.IA

• Title: Acoust.IA: l'Intelligence Artificielle au Service de l'Acoustique du Bâtiment
• Duration: Oct 2020 - Sep 2023
• Coordinator: Antoine Deleforge
• Participants: Antoine Deleforge, Cédric Foy, Stéphane Dilungana
• Abstract: This project aims at radically simplifying and improving the acoustic diagnosis of rooms and buildings using new techniques combining machine learning, signal processing and physics-based modeling.

InriaHub ADT PEGASUS

• Title: PEGASUS: rehaussement de la ParolE Généralisé par Apprentissage SUperviSé
• Duration: Nov 2020 - Oct 2022
• Coordinator: Antoine Deleforge
• Participants: Antoine Deleforge, Joris Cosentino, Manuel Pariente, Emmanuel Vincent
• Abstract: This engineering project aims at further developing, expanding and transfering the Asteroid speech enhancement and separation toolkit recently released by the team 51.

CPER LCHN

• Title: CPER “Langues, Connaissances et Humanités Numériques”
• Duration: 2015 - 2020
• Coordinator: Bruno Guillaume (LORIA) & Alain Polguère (ATILF)
• Participants: Dominique Fohr, Denis Jouvet, Odile Mella, Yves Laprie
• Abstract: The main goal is related to experimental platforms for supporting research activities in the domain of languages, knowledge and numeric humanities engineering. MULTISPEECH contributed to automatic speech recognition, speech-text alignment and prosody aspects.

ALOE Project (Région Grand-Est - Economie Numérique)

• Title: Logiciel éducatif Aloé 2.0
• Duration: Mar 2019 - Aug 2020
• Coordinator: Com-Medic (France)
• Partners: Com-Medic (France), MULTISPEECH (Inria, Nancy), 2LPN (Univ de Lorraine, Nancy), MJC / Centre Social Nomade (Vandoeuvre-Lès-Nancy)
• Participants: Denis Jouvet, Vincent Colotte, Slim Ouni, Louis Delebecque
• Abstract: ALOE is a method of reading relying on a specific representation of sounds. Our involvement in the project is to develop tools to translate automatically and align text sentences into phone sequences as required by the ALOE system, and to provide audio and video tutoring examples.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• A brief introduction to multichannel noise reduction with deep neural networks, 12th Speech in Noise Workshop, Toulouse, Jan 2020 (R. Serizel)
• Multimodal data acquisition and processing for spoken communication, Technologies du Langage Humain et Multimodalité, TLH-AFIA, Oct 2020 (S. Ouni)
• Language pathology, Séminaire Dépistage des troubles des apprentissages, EHESP, University of Sorbonne, Jan 2020 (A. Piquard-Kipffer)
• Screening and in-school caring for children with special educational needs – Dépistage et prise en charge des élèves à besoins éducatifs particuliers, Feb. 2020. INSPE & UIR, Casablanca, Morocco (A. Piquard-Kipffer)

11.1.5 Leadership within the scientific community

• Member of the Steering Committee of ISCA’s Special Interest Group on Security and Privacy in Speech Communication (E. Vincent).
• Member of the Steering Committee of the Detection and Classification of Acoustic Scenes and Events (DCASE) (R. Serizel)
• Secretary/Treasurer, executive member of AVISA (Auditory-VIsual Speech Association), an ISCA Special Interest Group (S. Ouni)

11.1.6 Scientific expertise

• Reviewer of ANR projects (D. Jouvet, Y. Laprie)
• Member of the Scientific Committee of an Institute for deaf children and teenagers, INJS-Metz (A. Piquard- Kipffer)

11.1.7 Research administration

• Member of Management board of Université de Lorraine (Y. Laprie)
• Head of the AM2I Scientific Pole of Université de Lorraine (Y. Laprie)
• Deputy Head of Science of Inria Nancy - Grand Est (E. Vincent)
• Scientific Director for the partnership between Inria and DFKI (E. Vincent)
• Co-Chair of the joint Inria-Loria Commission pour l'Action et la Responsabilité Ecologique (CARE, CLDD) (A. Deleforge)
• Member of Inria’s Evaluation Committee (E. Vincent)
• Member of the Comité Espace Transfert of Inria Nancy - Grand Est (E. Vincent)
• Member of the national hiring committee for Inria Junior Research Scientists (E. Vincent)
• Member of the hiring committee for Junior Research Scientists, Inria Rennes (E. Vincent)
• Member of Commission paritaire of Université de Lorraine (Y. Laprie)
• Member of the Commission de développement technologique of Inria Nancy - Grand Est (R. Serizel)
• Member of the Commission du personnel scientifique of Inria Nancy - Grand Est (R. Serizel)
• Member of a recruitment committee for Professor at ENIM-LCFC, Université de Lorraine (Y. Laprie)
• Member of a recruitment committee for Assistant Professor at Université Paris-Sud (D. Jouvet)
• Member of a recruitment committee for Assistant Professor at Le Mans Université (D. Jouvet)
• Member of the HCERES (Haut Conseil de l’évaluation de la recherche et de l’enseignement supérieur) evaluation committee for Gipsa-Lab, 2020 (S. Ouni)
• Member of the CNU-27 (Conseil National des Universités) - Computer Science (S. Ouni)
• Member of the Commission Information et Edition Scientifique (CIES) of Inria Nancy - Grand Est (A. Deleforge)

11.2.1 Teaching

• DUT: I. Illina, Java programming (100 hours), Linux programming (58 hours), and Advanced Java programming (40 hours), L1, University of Lorraine, France
• DUT: I. Illina, Supervision of student projects and internships (50 hours), L2, University of Lorraine, France
• DUT: R. Serizel, Introduction to office tools (108 hours), Multimedia and web (20 hours), Documents and databases (20 hours), L1, University of Lorraine, France
• DUT: R. Serizel, Multimedia content and indexing (14 hours), Content indexing and retrieval software (20 hours), L2, University of Lorraine, France
• DUT: S. Ouni, Programming in Java (24 hours), Web Programming (24 hours), Graphical User Interface (96 hours), L1, University of Lorraine, France
• DUT: S. Ouni, Advanced Algorihms (24 hours), L2, University of Lorraine, France
• Licence: A. Bonneau, Speech manipulations (2 hours), L1, Département d'orthophonie, University of Lorraine, France
• Licence: A. Bonneau, Phonetics (17 hours), L2, École d’audioprothèse, University of Lorraine, France
• Licence: V. Colotte, Digital literacy and tools (hybrid courses, 50 hours), L1, University of Lorraine, France
• Licence: V. Colotte, System (35 hours), L3, University of Lorraine, France
• Licence: O. Mella, Computer Networking (64 hours), L2-L3, University of Lorraine, France
• Licence: O. Mella, Introduction to Web Programming (24 hours) L1, University of Lorraine, France
• Licence: O. Mella, Digital tools (18 hours) L1, University of Lorraine, France
• Licence: A. Piquard-Kipffer, Education Science (40 hours), L1, Département d'orthophonie, University of Lorraine, France
• Licence: A. Piquard-Kipffer, Learning to Read (34 hours), L2, Département d'orthophonie, University of Lorraine, France
• Licence: A. Piquard-Kipffer , Psycholinguistics (20 hours), Departement Orthophonie, University Pierre et Marie Curie, Paris, France
• Licence: A. Piquard-Kipffer, Dyslexia, Dysorthographia (12 hours), L3, Département d'orthophonie, University of Lorraine, France
• Licence: A. Piquard-Kipffer, Mathematics Didactics, 9 hours, L3, Departement Orthophonie, University of Lorraine, France
• Master: V. Colotte, Introduction to Speech Analysis and Recognition (18 hours), M1, University of Lorraine, France
• Master: V. Colotte, Integration project: multimodal interaction with Pepper (15 hours), M2, University of Lorraine, France
• Master: D. Jouvet and S. Ouni, Multimodal oral comunication (24 hours), M2, University of Lorraine
• Master: Y. Laprie, Speech corpora (30 hours), M1, University of Lorraine, France
• Master: O. Mella, Computer Networking (10 hours), M1, University of Lorraine, France
• Master: S. Ouni, Multimedia in Distributed Information Systems (31 hours), M2, University of Lorraine
• Master: A. Piquard-Kipffer, Dyslexia, Dysorthographia diagnosis (9 hours), Deaf people & reading (21 hours), M1, Département d'orthophonie, University of Lorraine, France
• Master: A. Piquard-Kipffer, French Language Didactics (53 hours), M2, INSPE University of Lorraine, France
• Master: A. Piquard-Kipffer, Psychology (6 hours), M2, Departement of Psychology, University of Lorraine, France
• Executive Master : A.Piquard-Kipffer, Psychology, 12 hours, M2, Special Educational Needs with University of Lorraine, INSPÉ & UIR, International University of Rabat (Morocco)
• Master: R. Serizel and S. Ouni, Oral speech processing (24 hours), M2, University of Lorraine
• Master: E. Vincent and A. Kulkarni, Neural networks (38 hours), M2, University of Lorraine
• Continuous training: A. Piquard-Kipffer, Special Educational Needs (53 hours), INSPE,University of Lorraine, France
• Doctorat: A.Piquard-Kipffer , Language Pathology (20 hours), EHESP, University of Sorbonne, Paris, France
• Other: V. Colotte, Co-Responsible for NUMOC (Digital literacy by hybrid courses) for the University of Lorraine, France (for 7000 students)
• Other: S. Ouni, Responsible of Année Spéciale DUT, University of Lorraine

11.2.2 Supervision

• PhD: Amal Houidhek, “Synthèse paramétrique de parole arabe”, Fev 12, 2020 , cotutelle, V. Colotte, D. Jouvet and Z. Mnasri (ENIT, Tunisia) 87.
• PhD: Guillaume Carbajal, “Apprentissage profond bout-en-bout pour le rehaussement de la parole”, Université de Lorraine, Apr 24, 2020, R. Serizel, E. Vincent and É. Humbert (Invoxia) 84.
• PhD: Ioannis Douros, “Towards a 3 dimensional dynamic generic speaker model to study geometry simplifications of the vocal tract using magnetic resonance imaging data”, Sep 2, 2020, P.-A. Vuissoz (IADI) and Y. Laprie 86.
• PhD: Sunit Sivasankaran, “Localization guided speech separation”, Sep 4, 2020, D. Fohr and E. Vincent 88.
• PhD: Sara Dahmani, “Synthèse audiovisuelle de la parole expressive : modélisation des émotions par apprentissage profond”, Nov 13, 2020, S. Ouni and V. Colotte 85.
• PhD: Amine Menacer, “Traduction automatique de vidéos”, Nov 17, 2020, K. Smaïli (LORIA) and D. Jouvet.
• PhD: Diego Di Carlo, “Echo-aware signal processing for audio scene analysis”, Dec 4, 2020, A. Deleforge and N. Bertin (Inria Rennes).
• PhD in progress: Théo Biasutto, “Multimodal coarticulation modeling: Towards the animation of an intelligible speaking head”, S. Ouni.
• PhD in progress: Lou Lee, “Fonctions pragmatiques et prosodie de marqueurs discursifs en français et en anglais”, Oct 2017, Y. Keromnes (ATILF) and D. Jouvet.
• PhD in progress: Nicolas Turpault, “Deep learning for sound scene analysis in real environments”, Jan 2018, R. Serizel and E. Vincent.
• PhD in progress: Raphaël Duroselle, “Adaptation de domaine par réseaux de neurones appliquée au traitement de la parole”, Sep 2018, D. Jouvet and I. Illina.
• PhD in progress: Nicolas Furnon, “Deep-learning based speech enhancement with ad-hoc microphone arrays”, Oct 2018, R. Serizel, I. Illina and S. Essid (Télécom ParisTech).
• PhD in progress: Ajinkya Kulkarni, “Expressive speech synthesis by deep learning”, Oct. 2018, V. Colotte and D. Jouvet.
• PhD in progress: Manuel Pariente, “Deep learning-based phase-aware audio signal modeling and estimation”, Oct 2018, A. Deleforge and E. Vincent.
• PhD in progress: Adrien Dufraux, “Leveraging noisy, incomplete, or implicit labels for automatic speech recognition”, Nov 2018, E. Vincent, A. Brun (LORIA) and M. Douze (Facebook AI Research).
• PhD in progress: Ashwin Geet D'Sa, “Natural Language Processing: Online hate speech against migrants”, Apr 2019, I. Illina and D. Fohr.
• PhD in progress: Tulika Bose, “Online hate speech and topic classification”, Sep 2019, I. Illina, D. Fohr and A. Monnier (CREM).
• PhD in progress: Mauricio Michel Olvera Zambrano, “Robust audio event detection”, Oct 2019, E. Vincent and G. Gasso (LITIS).
• PhD in progress: Pierre Champion, “Privacy preserving and personalized transformations for speech recognition”, Oct 2019, D. Jouvet and A. Larcher (LIUM).
• PhD in progress: Shakeel Ahmad Sheikh, “Identifying disfluency in speakers with stuttering, and its rehabilitation, using DNN”, Oct 2019, S. Ouni.
• PhD in progress: Sandipana Dowerah, “Robust speaker verification from far-field speech”, Oct 2019, D. Jouvet and R. Serizel.
• PhD in progress: Xuechen Liu, “Robust speaker recognition for smart assistant technology”, Jan 2020, M. Sahidullah.
• PhD in progress: Georgios Zervakis, “Integration of symbolic knowledge into deep learning”, Nov 2019, M. Couceiro (LORIA) and E. Vincent.
• PhD in progress: Nicolas Zampieri, “Automatic classification using deep learning of hate speech posted on the Internet”, Nov. 2019, I. Illina and D. Fohr.
• PhD in progress: Prerak Srivastava, “Hearing the walls of a room: machine learning for audio augmented reality”, Oct 2020, A. Deleforge and E. Vincent.
• PhD in progress: Stéphane Dilungana, “L’intelligence artificielle au service du diagnostic acoustique : Apprendre à entendre les parois d’une salle”, Oct 2020, A. Deleforge, C. Foy (UMR AE) and S. Faisan (iCube)
• PhD in progress: Vinicius Souza Ribeiro, “Tracking articulatory contours in MR images and prediction of the vocal tract shape from a sequence of phonemes to be articulated”, Oct 2020, Y. Laprie.

11.2.3 Juries

11.3.1 Articles and contents

• Article “Peut-on faire confiance aux IA ?” in The Conversation, Nov 20, 2020 (E. Vincent) 97
• Interview “Protection de la vie privée : 2 outils de transformation de la voix et de texte”, Radio Village Innovation, Sep 16 & Oct 7, 2020 (E. Vincent)
• Interview for the CNIL White Paper “À votre écoute – Exploration des enjeux éthiques, techniques et juridiques des assistants vocaux”, Sep 7, 2020 (E. Vincent)
• Interview for France 3 Lorraine TV journal “Acoust.IA: le projet d'application destiné aux acousticiens”3, Dec 2020 (A. Deleforge)

11.3.2 Interventions

• Talk “Assistants vocaux, vie privée — Enjeux scientifiques et technologiques”, Meetup CNIL, Sep 2020 (E. Vincent)
• Animation of a round-table meeting “Langues des signes et numérique : quels défis, quels enjeux pour les apprentissages ?”, International Sign Language Day - Sep 2020, INJS-Metz (A. Piquard-Kipffer)
• Animation of a booth on “Teaching Robots to Hear Us” for Fête de la Science, Nancy, Oct 2020 (A. Deleforge)
• Participation to the Science-Theater project Binôme, compagnie Les Sens des Mots, Oct 2020 - Oct 2021 (A. Deleforge).
• Presentation of the project Audio Cockpit Denoising for voice Command at the Forum Innovation Defence, Dec 2020 (D. Fohr)
• Presentation of the project Audio Cockpit Denoising for voice Command to Florence Parly, Minister of the Armed Forces, Dec 2020 (I. Illina)
• Presentation of METAL project at JANE (journée académique du numérique), Feb 2020 (S. Ouni)
• Talk: “la scolarisation des élèves dyslexiques”, Training of trainers - Académie de Nancy-Metz & INSPE de l'Académie de Nancy-Metz, Jan 2020 (A.Piquard-Kipffer)

11.3.3 Creation of media or tools for science outreach

• Video “COMPRISE Voice Transformer”, https://www.youtube.com/watch?v=kh8no66BSDM
• Popular science blog post on group testing COVID-19, https://members.loria.fr/ADeleforge/les-maths-du-group-testing-melanger-des-prelevements-pour-accelerer-la-detection-du-covid-19/ (A. Deleforge)

International journals

• 6 article GuillaumeG. Carbajal, RomainR. Serizel, EmmanuelE. Vincent and EricE. Humbert. Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise IEEE/ACM Transactions on Audio, Speech and Language Processing July 2020
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• 7 article ZainebZ. Chelly Dagdia and ChristineC. Zarges. A detailed study of the distributed rough set based locality sensitive hashing feature selection technique Fundamenta Informaticae 2020
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• 8 article ZainebZ. Chelly Dagdia, ChristineC. Zarges, GaëlG. Beck and MustaphaM. Lebbah. A scalable and effective rough set theory-based approach for big data pre-processing Knowledge and Information Systems (KAIS) May 2020
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• 9 article SaraS. Dahmani, VincentV. Colotte and SlimS. Ouni. Some consideration on expressive audiovisual speech corpus acquisition using a multimodal platform Language Resources and Evaluation July 2020
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• 10 articleMathieuM. Fontaine, RolandR. Badeau and AntoineA. Liutkus. Separation of Alpha-Stable Random VectorsSignal ProcessingJanuary 2020, 107465
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• 11 article AshwinA. Geet D'Sa, IrinaI. Illina and DominiqueD. Fohr. Classification of Hate Speech Using Deep Neural Networks Revue d'Information Scientifique & Technique 25 01 December 2020
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• 12 article IrinaI. Illina and DominiqueD. Fohr. RNN Language Model Estimation for Out-of-Vocabulary Words Lecture Notes in Artificial Intelligence 12598 2020
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• 13 articleKarynaK. Isaieva, YvesY. Laprie, FreddyF. Odille, Ioannis KI. Douros, JacquesJ. Felblinger and Pierre-André PavP.-A. Vuissoz. Measurement of Tongue Tip Velocity from Real-Time MRI and Phase-Contrast Cine-MRI in Consonant ProductionJournal of Imaging65May 2020, 31
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• 14 articleKarynaK. Isaieva, YvesY. Laprie, NicolasN. Turpault, AlexisA. Houssard, JacquesJ. Felblinger and Pierre-AndréP.-A. Vuissoz. Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network ApproachApplied Artificial IntelligenceOctober 2020, 1-9
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• 15 article ManelM. Jerbi, ZainebZ. Chelly Dagdia, SlimS. Bechikh, MohamedM. Makhlouf and Lamjed BenL. Said. On the Use of Artificial Malicious Patterns for Android Malware Detection Computers and Security 92 2020
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• 16 articleTomiT. Kinnunen, HéctorH. Delgado, NicholasN. Evans, Kong-AikK.-A. Lee, VilleV. Vestman, AndreasA. Nautsch, MassimilianoM. Todisco, XinX. Wang, MdM. Sahidullah, JunichiJ. Yamagishi and Douglas AD. Reynolds. Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: FundamentalsIEEE/ACM Transactions on Audio, Speech and Language Processing28July 2020, 2195 - 2210
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• 17 article A. KishoreA. Kumar, DipjyotiD. Paul, MonisankhaM. Pal, MdM. Sahidullah and GoutamG. Saha. Speech Frame Selection for Spoofing Detection with an Application to Partially Spoofed Audio-Data International Journal of Speech Technology January 2021
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• 18 article SusantaS. Sarangi, MdM. Sahidullah and GoutamG. Saha. Optimization of data-driven filterbank for automatic speaker verification Digital Signal Processing 104 September 2020
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• 19 articleXinX. Wang, JunichiJ. Yamagishi, MassimilianoM. Todisco, HéctorH. Delgado, AndreasA. Nautsch, NicholasN. Evans, MdM. Sahidullah, VilleV. Vestman, TomiT. Kinnunen, Kong AikK. Lee, LauriL. Juvela, PaavoP. Alku, Yu-HuaiY.-H. Peng, Hsin-TeH.-T. Hwang, YuY. Tsao, Hsin-MinH.-M. Wang, SébastienS. Le Maguer, Sébastien LeS. Maguer, MarkusM. Becker, FergusF. Henderson, RobR. Clark, YuY. Zhang, QuanQ. Wang, YeY. Jia, KaiK. Onuma, KojiK. Mushika, TakashiT. Kaneda, YuanY. Jiang, Li-JuanL.-J. Liu, Yi-ChiaoY.-C. Wu, Wen-ChinW.-C. Huang, TomokiT. Toda, KouK. Tanaka, HirokazuH. Kameoka, IngmarI. Steiner, DrissD. Matrouf, Jean-FrançoisJ.-F. Bonastre, AvashnaA. Govender, SrikanthS. Ronanki, Jing-XuanJ.-X. Zhang and Zhen-HuaZ.-H. Ling. ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speechComputer Speech and Language64November 2020, 101114
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• 20 article ImeneI. Zangar, ZiedZ. Mnasri, VincentV. Colotte and DenisD. Jouvet. Duration modelling and evaluation for Arabic statistical parametric speech synthesis Multimedia Tools and Applications November 2020
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International peer-reviewed conferences

• 21 inproceedingsMohamedM. Amine Menacer, DominiqueD. Fohr, DenisD. Jouvet, KarimaK. Abidi, DavidD. Langlois and KamelK. Smaïli. AMIS project : automatic summarization and translation of videoActes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationauxNancy, France2020, 53-56
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• 22 inproceedingsCortoC. Bastien, AntoineA. Deleforge and CédricC. Foy. Mean Absorption Coefficient Estimation From Impulse Responses: Deep Learning vs. SabineForum Acusticum 2020Forum Acusticum 2020Virtual, Francehttps://fa2020.universite-lyon.fr/fa2020/english-version/welcome-46678.kjsp?RH=FA2020January 2020, 2
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• 23 inproceedings PierreP. Champion, DenisD. Jouvet and AnthonyA. Larcher. A Study of F0 Modification for X-Vector Based Speech Pseudonymization Across Gender The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence Virtual conference, China November 2020
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• 24 inproceedings SamueleS. Cornell, MichelM. Olvera, ManuelM. Pariente, GiovanniG. Pepe, EmanueleE. Principi, LeonardoL. Gabrielli and StefanoS. Squartini. Domain-Adversarial Training and Trainable Parallel Front-end for the DCASE 2020 Task 4 Sound Event Detection Challenge DCASE 2020 - 5th Workshop on Detection and Classification of Acoustic Scenes and Events Virtual, Japan November 2020
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• 25 inproceedings SamueleS. Cornell, MichelM. Olvera, ManuelM. Pariente, GiovanniG. Pepe, EmanueleE. Principi, LeonardoL. Gabrielli and StefanoS. Squartini. Task-Aware Separation for the DCASE 2020 Task 4 Sound Event Detection and Separation Challenge DCASE 2020 - 5th Workshop on Detection and Classification of Acoustic Scenes and Events Virtual, Japan March 2020
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• 26 inproceedings SamueleS. Cornell, MaurizioM. Omologo, StefanoS. Squartini and EmmanuelE. Vincent. Detecting and counting overlapping speakers in distant speech scenarios INTERSPEECH 2020 Shanghai, China October 2020
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• 27 inproceedings Ashwin GeetA. D'Sa, IrinaI. Illina and DominiqueD. Fohr. BERT and fastText Embeddings for Automatic Detection of Toxic Speech SIIE 2020 - Information Systems and Economic Intelligence Tunis, Tunisia February 2020
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• 28 inproceedings Ashwin GeetA. D'Sa, IrinaI. Illina, DominiqueD. Fohr, DietrichD. Klakow and DanaD. Ruiter. Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification Insights from Negative Results Workshop, EMNLP 2020 Punta Cana, Dominican Republic November 2020
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• 29 inproceedings DiegoD. Di Carlo, ClémentC. Elvira, AntoineA. Deleforge, NancyN. Bertin and RémiR. Gribonval. BLASTER: An Off-Grid Method for Blind and Regularized Acoustic Echoes Retrieval -- with supplementary material ICASSP 2020 - IEEE International Conference on Acoustic Speech and Signal Processing Barcelona, Spain Inria February 2020
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• 30 inproceedings Ioannis KI. Douros, ChrysanthiC. Dourou, YuY. Xie, JacquesJ. Felblinger, KarynaK. Isaieva, Pierre-AndréP.-A. Vuissoz and YvesY. Laprie. Synthesize MRI vocal tract data during CV production ISSP 2020 - 12th International Seminar on Speech Production Providence / Virtual, United States https://issp2020.yale.edu/ December 2020
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• 31 inproceedings Ioannis KI. Douros, AjinkyaA. Kulkarni, ChrysanthiC. Dourou, YuY. Xie, JacquesJ. Felblinger, KarynaK. Isaieva, Pierre-AndréP.-A. Vuissoz and YvesY. Laprie. Using Silence MR Image to Synthesise Dynamic MRI Vocal Tract Data of CV INTERSPEECH 2020 Shangaï / Virtual, China http://www.interspeech2020.org/ October 2020
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• 32 inproceedings Ioannis KI. Douros, AjinkyaA. Kulkarni, YuY. Xie, ChrysanthiC. Dourou, JacquesJ. Felblinger, KarynaK. Isaieva, Pierre-AndréP.-A. Vuissoz and YvesY. Laprie. MRI Vocal Tract Sagittal Slices Estimation during Speech Production of CV EUSIPCO 2020 - 28th European Signal Processing Conference Amsterdam / Virtual, Netherlands https://eusipco2020.org/ January 2021
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• 33 inproceedings Ioannis KI. Douros, YuY. Xie, ChrysanthiC. Dourou, JacquesJ. Felblinger, KarynaK. Isaieva, Pierre-AndréP.-A. Vuissoz and YvesY. Laprie. Vocal tract sagittal slices estimation from MRI midsagittal slices during speech production of CV ISSP 2020 - 12th International Seminar on Speech Production Providence / Virtual, United States https://issp2020.yale.edu/program.html December 2020
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• 34 inproceedings RaphaëlR. Duroselle, DenisD. Jouvet and IrinaI. Illina. Metric learning loss functions to reduce domain mismatch in the x-vector space for language recognition INTERSPEECH 2020 Shangaï / Virtual, China October 2020
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• 35 inproceedings RaphaëlR. Duroselle, DenisD. Jouvet and IrinaI. Illina. Unsupervised regularization of the embedding extractor for robust language identification Odyssey 2020 - The Speaker and Language Recognition Workshop Tokyo, Japan November 2020
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• 36 inproceedings AmélieA. Elmerich, AngeliqueA. Amelot, ShinjiS. Maeda, YvesY. Laprie, Jean FrancoisJ. Papon and LiseL. Crevier-Buchman. F1 and F2 measurements for French oral vowel with a new pneumotachograph mask ISSP 2020 - 12th International Seminar on Speech Production Providence / Virtual, United States https://issp2020.yale.edu/ December 2020
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• 37 inproceedings NicolasN. Furnon, RomainR. Serizel, IrinaI. Illina and SlimS. Essid. DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing Barcelona, Spain May 2020
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• 38 inproceedings NicolasN. Furnon, RomainR. Serizel, IrinaI. Illina and SlimS. Essid. Distributed speech separation in spatially unconstrained microphone arrays ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing Toronto, Canada June 2021
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• 39 inproceedings AshwinA. Geet D'Sa, IrinaI. Illina and DominiqueD. Fohr. Towards Non-Toxic Landscapes: Automatic Toxic Comment Detection Using DNN TRAC-2020, Second Workshop on Trolling, Aggression and Cyberbullying (LREC, 2020) Marseille, France May 2020
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• 40 inproceedings MathieuM. Hu, LaurentL. Pierron, EmmanuelE. Vincent and DenisD. Jouvet. Kaldi-web: An installation-free, on-device speech recognition system INTERSPEECH 2020 Show & Tell Shanghai, China October 2020
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• 41 inproceedings KarynaK. Isaieva, YvesY. Laprie, AlexisA. Houssard, JacquesJ. Felblinger and Pierre-AndréP.-A. Vuissoz. Tracking the tongue contours in rt-MRI films with an autoencoder DNN approach ISSP 2020 - 12th International Seminar on Speech Production Providence / Virtual, United States https://issp2020.yale.edu/ December 2020
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• 42 inproceedings AjinkyaA. Kulkarni, VincentV. Colotte and DenisD. Jouvet. Deep variational metric learning for transfer of expressivity in multispeaker text to Speech SLSP 2020 - 8th International Conference on Statistical Language and Speech Processing Cardiff / Virtual, United Kingdom October 2020
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• 43 inproceedings AjinkyaA. Kulkarni, VincentV. Colotte and DenisD. Jouvet. Transfer learning of the expressivity using flow metric learning in multispeaker text-to-speech synthesis INTERSPEECH 2020 Shanghai / Virtual, China October 2020
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• 44 inproceedings LouL. Lee, DenisD. Jouvet, KatarinaK. Bartkova, YvonY. Keromnes and MathildeM. Dargnat. Correlation between prosody and pragmatics: case study of discourse markers in French and English INTERSPEECH 2020 Shanghai, China October 2020
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• 45 inproceedings StephaneS. Level, IrinaI. Illina and DominiqueD. Fohr. Introduction of semantic model to help speech recognition TSD 2020 - Twenty-third International Conference on Text, Speech and Dialogue Brno, Czech Republic September 2020
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• 46 inproceedings XuechenX. Liu, MdM. Sahidullah and TomiT. Kinnunen. A Comparative Re-Assessment of Feature Extractors for Deep Speaker Embeddings INTERSPEECH 2020 Shanghai, China October 2020
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• 47 inproceedings XuechenX. Liu, MdM. Sahidullah and TomiT. Kinnunen. Learnable MFCCs for Speaker Verification 2021 IEEE International Symposium on Circuits and Systems (ISCAS) Daegu, South Korea May 2021
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• 48 inproceedings MohamedM. Maouche, Brij Mohan LalB. Srivastava, NathalieN. Vauquier, AurélienA. Bellet, MarcM. Tommasi and EmmanuelE. Vincent. A comparative study of speech anonymization metrics INTERSPEECH 2020 Shanghai, China October 2020
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• 49 inproceedings EstebanE. Marquer, AjinkyaA. Kulkarni and MiguelM. Couceiro. Embedding Formal Contexts Using Unordered Composition FCA4AI - 8th International Workshop "What can FCA do for Artificial Intelligence?" (colocated wit ECAI2020) Santiago de Compostela, Spain 2020
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• 50 inproceedings MichelM. Olvera, EmmanuelE. Vincent, RomainR. Serizel and GillesG. Gasso. Foreground-Background Ambient Sound Scene Separation 28th European Signal Processing Conference (EUSIPCO) Amsterdam, Netherlands January 2021
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• 51 inproceedings ManuelM. Pariente, SamueleS. Cornell, JorisJ. Cosentino, SunitS. Sivasankaran, EfthymiosE. Tzinis, JensJ. Heitkaemper, MichelM. Olvera, Fabian-RobertF.-R. Stöter, MathieuM. Hu, Juan M.J. Martı́n-Doñas, DavidD. Ditter, ArielA. Frank, AntoineA. Deleforge and EmmanuelE. Vincent. Asteroid: the PyTorch-based audio source separation toolkit for researchers Interspeech 2020 Shanghai, China http://interspeech2020.org/ October 2020
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• 52 inproceedings ManuelM. Pariente, SamueleS. Cornell, AntoineA. Deleforge and EmmanuelE. Vincent. Filterbank design for end-to-end speech separation ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing Barcelona, Spain May 2020
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• 53 inproceedings MdM. Sahidullah, AchintyaA. Kumar Sarkar, VilleV. Vestman, XuechenX. Liu, RomainR. Serizel, TomiT. Kinnunen, Zheng-HuaZ.-H. Tan and EmmanuelE. Vincent. UIAI System for Short-Duration Speaker Verification Challenge 2020 IEEE Spoken Language Technology Workshop 2021 Shenzhen, China Short-duration Speaker Verification Challenge 2020 January 2022
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• 54 inproceedings RomainR. Serizel, NicolasN. Turpault, AnkitA. Shah and JustinJ. Salamon. Sound event detection in synthetic domestic environments ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing Barcelona, Spain May 2020
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• 55 inproceedings ImranI. Sheikh, EmmanuelE. Vincent and IrinaI. Illina. On semi-supervised LF-MMI training of acoustic models with limited data INTERSPEECH 2020 Shanghai, China October 2020
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• 56 inproceedings PremjeetP. Singh, GoutamG. Saha and MdM. Sahidullah. Non-linear frequency warping using constant-Q transformation for speech emotion recognition 2021 International Conference on Computer Communication and Informatics (ICCCI -2021) Coimbatore, India January 2021
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• 57 inproceedings SunitS. Sivasankaran, EmmanuelE. Vincent and DominiqueD. Fohr. Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition 28th European Signal Processing Conference Amsterdam, Netherlands January 2021
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• 58 inproceedings SunitS. Sivasankaran, EmmanuelE. Vincent and DominiqueD. Fohr. SLOGD: Speaker Location Guided Deflation Approach to Speech Separation ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing Barcelona, Spain May 2020
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• 59 inproceedings Brij Mohan LalB. Srivastava, NataliaN. Tomashenko, XinX. Wang, EmmanuelE. Vincent, JunichiJ. Yamagishi, MohamedM. Maouche, AurélienA. Bellet and MarcM. Tommasi. Design Choices for X-vector Based Speaker Anonymization INTERSPEECH 2020 Shanghai, China October 2020
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• 60 inproceedingsBrij Mohan LalB. Srivastava, NathalieN. Vauquier, MdM. Sahidullah, AurélienA. Bellet, MarcM. Tommasi and EmmanuelE. Vincent. Evaluating Voice Conversion-based Privacy Protection against Informed AttackersICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal ProcessingBarcelona, SpainMay 2020, 2802-2806
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• 61 inproceedings NataliaN. Tomashenko, Brij Mohan LalB. Srivastava, XinX. Wang, EmmanuelE. Vincent, AndreasA. Nautsch, JunichiJ. Yamagishi, NicholasN. Evans, JoseJ. Patino, Jean-FrançoisJ.-F. Bonastre, Paul-GauthierP.-G. Noé and MassimilianoM. Todisco. Introducing the VoicePrivacy initiative INTERSPEECH 2020 Shanghai, China October 2020
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• 62 inproceedings AnastasiiaA. Tsukanova, Ioannis KI. Douros and YvesY. Laprie. DNN-Based Parametric Speech Synthesis Enhanced With Articulatory Information ISSP 2020 - 12th International Seminar on Speech Production Providence / Virtual, United States https://issp2020.yale.edu/ December 2020
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• 63 inproceedings Mehmet Ali TuğtekinM. Turan, EmmanuelE. Vincent and DenisD. Jouvet. Achieving multi-accent ASR via unsupervised acoustic model adaptation INTERSPEECH 2020 Shanghai, China October 2020
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• 64 inproceedings NicolasN. Turpault and RomainR. Serizel. Training Sound Event Detection On A Heterogeneous Dataset DCASE Workshop Tokyo, Japan March 2020
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• 65 inproceedings NicolasN. Turpault, RomainR. Serizel and EmmanuelE. Vincent. Limitations of weak labels for embedding and tagging ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing Barcelona, Spain May 2020
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• 66 inproceedings NicolasN. Turpault, ScottS. Wisdom, HakanH. Erdogan, John RJ. Hershey, RomainR. Serizel, EduardoE. Fonseca, PremP. Seetharaman and JustinJ. Salamon. Improving Sound Event Detection In Domestic Environments Using Sound Separation DCASE Workshop 2020 - Detection and Classification of Acoustic Scenes and Events Tokyo / Virtual, Japan March 2020
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• 67 inproceedings ShinjiS. Watanabe, MichaelM. Mandel, JonJ. Barker, EmmanuelE. Vincent, AshishA. Arora, XuankaiX. Chang, SanjeevS. Khudanpur, VimalV. Manohar, DanielD. Povey, DeshD. Raj, DavidD. Snyder, Aswin ShanmugamA. Subramanian, JanJ. Trmal, Bar BenB. Yair, ChristophC. Boeddeker, ZhaohengZ. Ni, YusukeY. Fujita, ShotaS. Horiguchi, NaoyukiN. Kanda, TakuyaT. Yoshioka and NevilleN. Ryant. CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings CHiME 2020 - 6th International Workshop on Speech Processing in Everyday Environments Barcelona / Virtual, Spain May 2020
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• 68 inproceedings MoritzM. Wolf, DanaD. Ruiter, Ashwin GeetA. D'Sa, LianeL. Reiners, JanJ. Alexandersson and DietrichD. Klakow. HUMAN: Hierarchical Universal Modular ANnotator EMNLP 2020 System Demonstration Punta Cana (Virtual), Dominican Republic November 2020
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• 69 inproceedings PalomaP. de las Cuevas, PabloP. Garcia-Sanchez, ZainebZ. Chelly Dagdia, Maria-IsabelM.-I. Garcia-Arenas and Juan JulianJ. Merelo. Automatic rule extraction from access rules using Genetic Programming EvoCOP 2020 - 20th European Conference on Evolutionary Computation in Combinatorial Optimisation Seville, Spain http://www.evostar.org/2020/ April 2020
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National peer-reviewed Conferences

• 70 inproceedingsSaraS. Dahmani, VincentV. Colotte and SlimS. Ouni. Étude comparative des paramètres d'entrée pour la synthèse expressive audiovisuelle de la parole par DNNsActes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la ParoleNancy, France2020, 127-135
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• 71 inproceedingsRaphaëlR. Duroselle, DenisD. Jouvet and IrinaI. Illina. Adaptation de domaine non supervisée pour la reconnaissance de la langue par régularisation d'un réseau de neuronesActes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la ParoleNancy, France2020, 190-198
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• 72 inproceedingsLouL. Lee, DenisD. Jouvet, KatarinaK. Bartkova, YvonY. Keromnes and MathildeM. Dargnat. Étude comparative de corrélats prosodiques de marqueurs discursifs français et anglais selon leur fonction pragmatiqueActes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la ParoleNancy, France2020, 335-343
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• 73 inproceedingsStephaneS. Level, IrinaI. Illina and DominiqueD. Fohr. Introduction d’informations sémantiques dans un système de reconnaissance de la paroleActes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la ParoleNancy, France2020, 362-369
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Conferences without proceedings

• 74 inproceedings A. KishoreA. Kumar, ShefaliS. Waldekar, GoutamG. Saha and MdM. Sahidullah. Domain-Dependent Speaker Diarization for the Third DIHARD Challenge The Third DIHARD Speech Diarization Challenge Workshop (Virtual) (Virtual), France January 2021
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• 75 inproceedings StephaneS. Level, IrinaI. Illina and DominiqueD. Fohr. Semantic Context Model for Efficient Speech Recognition ICCAS 2020 - The first International Conference on Cognitive Aircraft Systems Toulouse, France March 2020
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• 76 inproceedings RomainR. Serizel. A brief introduction to multichannel noise reduction with deep neural networks SpiN 2020 - 12th Speech in Noise Workshop Toulouse, France January 2020
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• 77 inproceedings NicolasN. Zampieri, IrinaI. Illina and DominiqueD. Fohr. A comparative study of different features for efficient automatic hate speech detection 17th International Pragmatics Conference Winterthur, Switzerland June 2021
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Scientific book chapters

• 78 inbookZainebZ. Chelly Dagdia and MiroslavM. Mirchev. When Evolutionary Computing Meets Astro- and GeoinformaticsKnowledge Discovery in Big Data from Astronomy and Earth Observationhttps://www.sciencedirect.com/science/article/pii/B9780128191545000266May 2020, 283-306
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• 79 inbook AlainA. Lelu and MartineM. Cadot. Importance of Dataspace Embeddings when Evaluating Text Clustering Methods Data Analysis and Rationality in a Complex World December 2020
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Edition (books, proceedings, special issue of a journal)

• 80 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole Nancy, France 2020
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• 81 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 3 : Rencontre des Étudiants Chercheurs en Informatique pour le TAL. JEP-TALN-RECITAL 2020 : 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition) 3 Volume 3 : Rencontre des Étudiants Chercheurs en Informatique pour le TAL Nancy, France http://talnarchives.atala.org/RECITAL/RECITAL-2020/ 2020
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• 82 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux JEP-TALN-RECITAL 2020 4 Volume 4 : Démonstrations et résumés d'articles internationaux Nancy, France http://talnarchives.atala.org/TALN/TALN-2020/ 2020
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• 83 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles JEP-TALN-RECITAL 2020 2 Volume 2 : Traitement Automatique des Langues Naturelles Nancy, France http://talnarchives.atala.org/TALN/TALN-2020/ 2020
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Doctoral dissertations and habilitation theses

• 84 thesis GuillaumeG. Carbajal. End-to-end deep learning for speech enhancement Université de Lorraine April 2020
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• 85 thesis SaraS. Dahmani. Audiovisual synthesis of expressive speech : modeling of emotions with deep learning Université de Lorraine November 2020
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• 86 thesis Ioannis KI. Douros. Towards a 3 dimensional dynamic generic speaker model to study geometry simplifications of the vocal tract using magnetic resonance imaging data Université de Lorraine September 2020
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• 87 thesis AmalA. Houidhek. Parametric synthesis of Arabic speech Université de Lorraine; Université de Tunis El Manar (Tunisie) February 2020
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• 88 thesis SunitS. Sivasankaran. Localization guided speech separation Université de Lorraine September 2020
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Reports & preprints

• 89 report PierreP. Champion, DenisD. Jouvet and AnthonyA. Larcher. Speaker information modification in the VoicePrivacy 2020 toolchain INRIA Nancy, équipe Multispeech; LIUM - Laboratoire d'Informatique de l'Université du Mans November 2020
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• 90 misc GiacomoG. Ferroni, NicolasN. Turpault, JuanJ. Azcarreta, FrancescoF. Tuveri, RomainR. Serizel, ÇagdaşÇ. Bilen and SachaS. Krstulović. Improving Sound Event Detection Metrics: Insights from DCASE 2020 October 2020
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• 91 misc NicolasN. Furnon, RomainR. Serizel, IrinaI. Illina and SlimS. Essid. DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays November 2020
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• 92 misc AjinkyaA. Kulkarni, VincentV. Colotte and DenisD. Jouvet. Improving Latent Representation For End To End Multispeaker Expressive Text To Speech System October 2020
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• 93 misc AjinkyaA. Kulkarni, Ioannis KI. Douros, VincentV. Colotte and DenisD. Jouvet. Emotion recognition from phoneme-duration information October 2020
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• 94 misc A. KishoreA. Kumar, ShefaliS. Waldekar, GoutamG. Saha and MdM. Sahidullah. ABSP System for The Third DIHARD Challenge February 2021
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• 95 misc NicolasN. Turpault, RomainR. Serizel, ScottS. Wisdom, HakanH. Erdogan, John RJ. Hershey, EduardoE. Fonseca, PremP. Seetharaman and JustinJ. Salamon. Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes October 2020
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• 96 misc ScottS. Wisdom, HakanH. Erdogan, Daniel P WD. Ellis, RomainR. Serizel, NicolasN. Turpault, EduardoE. Fonseca, JustinJ. Salamon, PremP. Seetharaman and John RJ. Hershey. What’s All the FUSS About Free Universal Sound Separation Data? October 2020
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Scientific popularization

• 97 article E. Vincent. Peut-on faire confiance aux IA ? The Conversation November 2020
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Softwares

• 98 software S. Sivasankaran, I. Illina and E. Vincent. voiceHome-2 corpus - automatic speech recognition baseline - scripts hello October 2020
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