Projects

AISSPER: Artificial Intelligence for Semantically controlled SPEech undeRstanding Artificial Intelligence (AI) holds strategic importance at the national level due to impressive outcomes achieved by deep learning algorithms in various domains such as natural language processing (NLP), medicine, and political analytics across a wide range of applications. France has emerged as a leader in deep learning owing to recent political efforts highlighted in recent years. Over the last decade, substantial efforts have been dedicated to end-to-end Spoken Language Understanding (SLU) systems, driven by the feasibility of applications like personal assistants and conversational systems. Superior results have been observed in automatic speech recognition (ASR) with architectures based on hyper-complex number algebra called quaternions, requiring less processing time (Morchid 2018) and fewer parameters to estimate compared to conventional models (Parcollet et al 2018; 2019). Reducing model parameters efficiently trains neural architectures with limited data quantities, often challenging to obtain for specific semantic concepts and contexts from specific domains. Intrinsically linked learning processes like ASR and SLU hinder the parallelization of learning examples, critical for lengthy sequences as memory constraints limit batch processing using examples. Furthermore, error analysis conducted on completed projects like M2CR, JOKER, VERA, SUMACC, Media, or DECODA highlighted the importance of Plus d'infos

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

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

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