17 citations · 18 across the 10 of their papers we have counts for
10 papers
The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production
Guilhem Fauré, Mostafa Sadeghi, Sam Bigeard +1
Latent diffusion approaches to sign language production (SLP) rely on an initial stage that learns an encoding of sign pose sequences, enabling generative modeling in the resulting…
Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement
Colombe Mboungou, Mostafa Sadeghi, Jean-Eudes Ayilo +1
Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments. Recent work introduced diffusion-based unsupervised AVSE,…
Diffusion-based Frameworks for Unsupervised Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper addresses unsupervised diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speec…
Posterior Transition Modeling for Unsupervised Diffusion-Based Speech Enhancement
Mostafa Sadeghi, Jean-Eudes Ayilo, Romain Serizel +1
We explore unsupervised speech enhancement using diffusion models as expressive generative priors for clean speech. Existing approaches guide the reverse diffusion process using no…
Diffusion-based Unsupervised Audio-visual Speech Enhancement
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel +1
This paper proposes a new unsupervised audio-visual speech enhancement (AVSE) approach that combines a diffusion-based audio-visual speech generative model with a non-negative matr…
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…
Diffusion-based speech enhancement with a weighted generative-supervised learning loss
Jean-Eudes Ayilo, Mostafa Sadeghi, Romain Serizel
Diffusion-based generative models have recently gained attention in speech enhancement (SE), providing an alternative to conventional supervised methods. These models transform cle…
Unsupervised speech enhancement with diffusion-based generative models
Berné Nortier, Mostafa Sadeghi, Romain Serizel
Recently, conditional score-based diffusion models have gained significant attention in the field of supervised speech enhancement, yielding state-of-the-art performance. However,…
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder
Mostafa Sadeghi, Romain Serizel
In this paper, we address the unsupervised speech enhancement problem based on recurrent variational autoencoder (RVAE). This approach offers promising generalization performance o…
Unsupervised Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence Test
Mostafa Sadeghi, Xavier Alameda-Pineda, Radu Horaud
This paper addresses the problem of analysing the performance of 3D face alignment (3DFA), or facial landmark localization. This task is usually supervised, based on annotated data…