11 citations · 21 across the 5 of their papers we have counts for
5 papers
Data-Driven Decoding of Russell's Circumplex Model of Affect
Amdjed Belaref, Samir Sadok, Zineb Noumir +1
Affective computing increasingly relies on deep learning to represent emotions, yet latent spaces often remain opaque, high-dimensional black boxes. This paper investigates whether…
Motion-DVAE: Unsupervised learning for fast human motion denoising
Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +1
Pose and motion priors are crucial for recovering realistic and accurate human motion from noisy observations. Substantial progress has been made on pose and shape estimation from…
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…
A vector quantized masked autoencoder for speech emotion recognition
Samir Sadok, Simon Leglaive, Renaud Séguier
Recent years have seen remarkable progress in speech emotion recognition (SER), thanks to advances in deep learning techniques. However, the limited availability of labeled data re…
Learning and controlling the source-filter representation of speech with a variational autoencoder
Samir Sadok, Simon Leglaive, Laurent Girin +2
Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of da…