19 citations · 40 across the 11 of their papers we have counts for
13 papers
GLaS-JEPA: Gaussian-Regularized Speech SSL without Engineered Prediction Targets
Gaspard Botté, Séverin Baroudi, Samir Sadok +5
Speech self-supervised learning aims to learn general-purpose representations for downstream speech tasks. However, current approaches rely on complex, carefully designed predictio…
InsideSSL: Understanding Self-Supervised Speech Representations using a Model-Centric Perspective
Samir Sadok, Xavier Alameda-Pineda
Self-supervised learning (SSL) models, such as Wav2Vec2, HuBERT, and WavLM, have become foundational across a wide range of speech and audio tasks. Despite their success, understan…
HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models
Artem Ploujnikov, Francesco Verdini, Samir Sadok +1
Discrete audio representations have become increasingly popular for building multimodal text-audio systems and integrating audio capabilities into Large Language Models (LLMs). How…
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…
From Tokens to Faces: Investigating Discrete Speech Representations for 3D Facial Animation
Pedro Correa, Olivier Perrotin, Samir Sadok +2
The choice of speech representation is critical in speech-driven 3D facial animation. Representations differ in what they encode: SSL features emphasize segmental and semantic cues…
The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs
Samir Sadok, Laurent Girin, Xavier Alameda-Pineda
Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly within the same latent space. As a result,…
Residual Tokens Enhance Masked Autoencoders for Speech Modeling
Samir Sadok, Stéphane Lathuilière, Xavier Alameda-Pineda
Recent speech modeling relies on explicit attributes such as pitch, content, and speaker identity, but these alone cannot capture the full richness of natural speech. We introduce…
Bringing Interpretability to Neural Audio Codecs
Samir Sadok, Julien Hauret, Éric Bavu
The advent of neural audio codecs has increased in popularity due to their potential for efficiently modeling audio with transformers. Such advanced codecs represent audio from a h…
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…
A multimodal dynamical variational autoencoder for audiovisual speech representation learning
Samir Sadok, Simon Leglaive, Laurent Girin +2
In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to unsupervised audio-visual speech representation learning. The latent space is structured to dissociate 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…
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…