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arXiv · 2408.11562

A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification

Xujiang Xing·Mingxing Xu·Thomas Fang Zheng
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Abstract

Automatic Speaker Verification (ASV) suffers from performance degradation in noisy conditions. To address this issue, we propose a novel adversarial learning framework that incorporates noise-disentanglement to establish a noise-independent speaker invariant embedding space. Specifically, the disentanglement module includes two encoders for separating speaker related and irrelevant information, respectively. The reconstruction module serves as a regularization term to constrain the noise. A feature-robust loss is also used to supervise the speaker encoder to learn noise-independent speaker embeddings without losing speaker information. In addition, adversarial training is introduced to discourage the speaker encoder from encoding acoustic condition information for achieving a speaker-invariant embedding space. Experiments on VoxCeleb1 indicate that the proposed method improves the performance of the speaker verification system under both clean and noisy conditions.

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Xujiang Xing, Mingxing Xu, Thomas Fang Zheng. 2024-08-21. A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification. https://doi.org/10.21437/interspeech.2024-700

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