4 papers
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…
MEGA: Masked Generative Autoencoder for Human Mesh Recovery
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +1
Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless,…
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…