DAPADAF-E Project
Validity of a task of acoustic-phonetic decoding on anatomic deficits in paramedical assessment of speech disorders for patients treated for oral or oropharyngeal cancer Plus d'infos
Validity of a task of acoustic-phonetic decoding on anatomic deficits in paramedical assessment of speech disorders for patients treated for oral or oropharyngeal cancer Plus d'infos
Development of a methodology for evaluating voice identification systems The BRUEL project concerns the evaluation/certification of voice identification systems against adversarial attacks. Indeed, speaker recognition systems are vulnerable not only to speech artificially produced by voice synthesis but also to other forms of attacks such as voice identity conversion and replay attacks. The artifacts created during the creation or manipulation of these fraudulent attacks leave marks in the signal by voice synthesis algorithms, thus distinguishing the original real voice from a forged voice. Under these conditions, detecting identity theft requires evaluating identity theft countermeasures concurrently with speaker recognition systems. The BRUEL project aims to propose the first methodology for evaluating/certifying voice identification systems based on a Common Criteria approach. List of partners: CEA Eurecom Service National de Police Scientifique IRCAM LIA Laboratoire d’Informatique d’Avignon Project Coordinator: LIA Scientific Manager for LIA: Driss Matrouf Start Date: 01/01/2023 End Date: 30/06/2026 More
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
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
Laboratoire Informatique d'Avignon — Avignon Université