UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917444049698816 |
|---|---|
| author | Gan, Chong-Xin Bell, Peter Mak, Man-Wai Li, Zhe Jin, Zezhong Huang, Zilong Lee, Kong Aik |
| author_facet | Gan, Chong-Xin Bell, Peter Mak, Man-Wai Li, Zhe Jin, Zezhong Huang, Zilong Lee, Kong Aik |
| contents | The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm often fails to leverage the generalization and robustness benefits inherent in large-scale speech enhancement pre-training. Moreover, maintaining the speaker information in the denoised speech is not an explicit objective of the speech enhancement process. To address these limitations, we proposed a scalable \textbf{U}Net-based \textbf{F}usion framework (UF-EMA) that considers the noisy and enhanced speech as a multi-channel input, thereby enabling the speaker encoder to exploit speaker information effectively. In addition, an \textbf{E}xponential \textbf{M}oving \textbf{A}verage strategy is applied to a speaker encoder pre-trained on clean speech to mitigate overfitting and facilitate a smooth transition from clean to noisy conditions. Experimental results on multiple noise-contaminated test sets showcase the superiority of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25624 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition Gan, Chong-Xin Bell, Peter Mak, Man-Wai Li, Zhe Jin, Zezhong Huang, Zilong Lee, Kong Aik Audio and Speech Processing The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm often fails to leverage the generalization and robustness benefits inherent in large-scale speech enhancement pre-training. Moreover, maintaining the speaker information in the denoised speech is not an explicit objective of the speech enhancement process. To address these limitations, we proposed a scalable \textbf{U}Net-based \textbf{F}usion framework (UF-EMA) that considers the noisy and enhanced speech as a multi-channel input, thereby enabling the speaker encoder to exploit speaker information effectively. In addition, an \textbf{E}xponential \textbf{M}oving \textbf{A}verage strategy is applied to a speaker encoder pre-trained on clean speech to mitigate overfitting and facilitate a smooth transition from clean to noisy conditions. Experimental results on multiple noise-contaminated test sets showcase the superiority of the proposed approach. |
| title | UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2604.25624 |