4 papers · 1 filter
Modeling strategies for speech enhancement in the latent space of a neural audio codec
Sofiene Kammoun, Xavier Alameda-Pineda, Simon Leglaive
Neural audio codecs (NACs) provide compact latent speech representations in the form of sequences of continuous vectors or discrete tokens. In this work, we investigate how these t…
A vector quantized masked autoencoder for audiovisual speech emotion recognition
Samir Sadok, Simon Leglaive, Renaud Séguier
An important challenge in emotion recognition is to develop methods that can leverage unlabeled training data. In this paper, we propose the VQ-MAE-AV model, a self-supervised mult…
AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder
Samir Sadok, Simon Leglaive, Laurent Girin +2
This article introduces AnCoGen, a novel method that leverages a masked autoencoder to unify the analysis, control, and generation of speech signals within a single model. AnCoGen…
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
Simon Leglaive, Matthieu Fraticelli, Hend ElGhazaly +7
Supervised models for speech enhancement are trained using artificially generated mixtures of clean speech and noise signals. However, the synthetic training conditions may not acc…