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PhD defense of Adrien Gresse – 6 February 2019

6 February 2019

Thursday 6 February 2020 14:30 at CERI (Amphitheater Ada). Title: “The Art of Voice: Characterizing Vocal Information in Artistic Choices” Jury members: Mr. Emmanuel Vincent, Research Director at Inria-Nancy, LORIA, Reviewer Mr. Jean-Julien Aucouturier, Research Scientist at CNRS, IRCAM, Reviewer Ms. Julie Mauclair, Assistant Professor at the University of Toulouse, IRIT, Examiner Ms. Lori Lamel, Research Director at CNRS, LIMSI, Examiner Mr. Yannick Estève, Professor at the University of Avignon, LIA, Examiner Mr. Jean-François Bonastre, Professor at the University of Avignon, LIA, Thesis Supervisor Mr. Richard Dufour, Assistant Professor at the University of Avignon, LIA, Co-supervisor Mr. Vincent Labatut, Assistant Professor at the University of Avignon, LIA, Co-supervisor Abstract: To reach an international audience, audiovisual productions (films, series, video games) need to be translated into other languages. Often, the original language voices in the work are replaced by new voices in the target language. The vocal casting process aiming to choose a voice (an actor) in accordance with the original voice and the character played is manually performed by an artistic director (AD). Today, ADs have a tendency for new “talents” (less expensive and more available than experienced dubbers), but they cannot conduct large-scale auditions. Providing audiovisual industry professionals with Plus d'infos

ANR Project VoicePersonae

1 February 2019

With recent advancements in automatic speech and language processing, humans are increasingly interacting vocally with intelligent artificial agents. The use of voice in applications is expanding rapidly, and this mode of interaction is becoming more widely accepted. Nowadays, vocal systems can offer synthesized messages of such quality that discerning them from human-recorded messages is difficult. They are also capable of understanding requests expressed in natural language, albeit within their specific application framework. Furthermore, these systems frequently recognize or identify their users by their voices. Plus d'infos

ANR RUGBI Project

1 February 2019

Searching for Linguistic Units to Improve the Measurement of Speech Intelligibility Altered by Pathological Production Disorders In the context of speech production disorders observed in ORL cancers, neurological, sensory, or structural pathologies, the goal of the RUGBI project is to enhance the measurement of intelligibility deficits. List of Partners: IRIT Institut de Recherche en Informatique de Toulouse CHU Toulouse Direction de la Recherche LPL Laboratoire Parole et Langage LIA Laboratoire d’Informatique d’Avignon OCTOGONE UNITE DE RECHERCHE INTERDISCIPLINAIRE OCTOGON Project Coordinator: Jérome Farinas (IRIT) Scientific Lead for LIA: Corinne Fredouille Period: 2019-2022 More

ANR ROBOVOX Project

1 February 2019

Robust Vocal Identification for Mobile Security Robots This project focuses on robust vocal identification for mobile security robots and proposes solutions integrating supplementary modalities to voice recognition, leveraging the context of human-robot interaction. List of Partners: INRIA Grand Est AI Mergence LIA Laboratoire d’Informatique d’Avignon Project Coordinator: LIA Scientific Manager for LIA: Driss Matrouf Start Date: 01/02/2019 End Date: 30/04/2024 More

ANR DEEP-PRIVACY Project

1 January 2019

Distributed, Personalized, Privacy-Preserving Learning for Speech Processing The project focuses on developing distributed, personalized, and privacy-preserving approaches for speech recognition. We propose an approach where each user’s device locally performs private computations and does not share raw voice data, while certain inter-user computations (such as model enrichment) are conducted on a server or a peer-to-peer network, with voice data shared after anonymization. Objectives: Speech recognition is now used in numerous applications, including virtual assistants that collect, process, and store personal voice data on centralized servers, raising serious privacy concerns. The use of embedded speech recognition addresses these privacy aspects, but only during the speech recognition phase. However, there is still a need to further improve speech recognition technology as its performance remains limited in adverse conditions (e.g., noisy environments, reverberant speech, strong accents, etc.). This can only be achieved from large speech corpora representative of real and diverse usage conditions. Hence, there is a necessity to share voice data while ensuring privacy. Improvements obtained through shared voice data will then benefit all users. <br /><br />In this context, DEEP-PRIVACY proposes a new paradigm based on a distributed, personalized, and privacy-preserving approach. Some processing occurs on the user’s terminal, ensuring privacy Plus d'infos

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Laboratoire Informatique d'Avignon — Avignon Université