Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions

Résumé

The paper introduces Diff-Filter, a multichannel speech enhancement approach based on the diffusion probabilistic model, for improving speaker verification performance under noisy and reverberant conditions. It also presents a new two-step training procedure that takes the benefit of self-supervised learning. In the first stage, the Diff-Filter is trained by conducting timedomain speech filtering using a scoring-based diffusion model. In the second stage, the Diff-Filter is jointly optimized with a pre-trained ECAPA-TDNN speaker verification model under a self-supervised learning framework. We present a novel loss based on equal error rate. This loss is used to conduct selfsupervised learning on a dataset that is not labelled in terms of speakers. The proposed approach is evaluated on MultiSV, a multichannel speaker verification dataset, and shows significant improvements in performance under noisy multichannel conditions.
Fichier principal
Vignette du fichier
interspeech.pdf (544.02 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04151411 , version 1 (05-07-2023)

Identifiants

Citer

Sandipana Dowerah, Ajinkya Kulkarni, Romain Serizel, Denis Jouvet. Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions. INTERSPEECH 2023, Aug 2023, Dublin (Ireland), Ireland. pp.3849-3853, ⟨10.21437/Interspeech.2023-1890⟩. ⟨hal-04151411v1⟩
39 Consultations
86 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More