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Conference Papers Year : 2023

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

Abstract

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.
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Dates and versions

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

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Cite

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⟩
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