336 citations · 1.4k across the 70 of their papers we have counts for
7 papers · 1 filter
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…
Unsupervised speech enhancement with deep dynamical generative speech and noise models
Xiaoyu Lin, Simon Leglaive, Laurent Girin +1
This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorizat…
Speech Modeling with a Hierarchical Transformer Dynamical VAE
Xiaoyu Lin, Xiaoyu Bie, Simon Leglaive +2
The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep generative models that extends the VAE to model a sequence of observed data and a corresponding…
Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement
Mostafa Sadeghi, Xavier Alameda-Pineda
Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to…
Deep Variational Generative Models for Audio-visual Speech Separation
Viet-Nhat Nguyen, Mostafa Sadeghi, Elisa Ricci +1
In this paper, we are interested in audio-visual speech separation given a single-channel audio recording as well as visual information (lips movements) associated with each speake…
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
Mostafa Sadeghi, Xavier Alameda-Pineda
In this paper, we are interested in unsupervised (unknown noise) audio-visual speech enhancement based on variational autoencoders (VAEs), where the probability distribution of cle…
Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders
Mostafa Sadeghi, Xavier Alameda-Pineda
Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a nonnegative matrix factorization (NMF) model fo…