A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification
<|reference_start|>A Joint Noise Disentanglement and Adversarial Training Framework for Robust Speaker Verification: 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.<|reference_end|>
@article{xing2024a,
    title={A Joint Noise Disentanglement and Adversarial Training Framework for
  Robust Speaker Verification},
    author={Xujiang Xing, Mingxing Xu, Thomas Fang Zheng},
    journal={Interspeech2024},
    year={2024},
    doi={10.21437/Interspeech.2024-700},
    number={707-711},
    archivePrefix={arXiv},
    eprint={2408.11562},
    primaryClass={cs.SD eess.AS}
}