SaD: A Scenario-Aware Discriminator for Speech Enhancement
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912577483702272 |
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| author | Yuan, Xihao Liu, Siqi Chen, Yan Zhou, Hang Liu, Chang Chen, Hanting Hu, Jie |
| author_facet | Yuan, Xihao Liu, Siqi Chen, Yan Zhou, Hang Liu, Chang Chen, Hanting Hu, Jie |
| contents | Generative adversarial network-based models have shown remarkable performance in the field of speech enhancement. However, the current optimization strategies for these models predominantly focus on refining the architecture of the generator or enhancing the quality evaluation metrics of the discriminator. This approach often overlooks the rich contextual information inherent in diverse scenarios. In this paper, we propose a scenario-aware discriminator that captures scene-specific features and performs frequency-domain division, thereby enabling a more accurate quality assessment of the enhanced speech generated by the generator. We conducted comprehensive experiments on three representative models using two publicly available datasets. The results demonstrate that our method can effectively adapt to various generator architectures without altering their structure, thereby unlocking further performance gains in speech enhancement across different scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SaD: A Scenario-Aware Discriminator for Speech Enhancement Yuan, Xihao Liu, Siqi Chen, Yan Zhou, Hang Liu, Chang Chen, Hanting Hu, Jie Sound Audio and Speech Processing Generative adversarial network-based models have shown remarkable performance in the field of speech enhancement. However, the current optimization strategies for these models predominantly focus on refining the architecture of the generator or enhancing the quality evaluation metrics of the discriminator. This approach often overlooks the rich contextual information inherent in diverse scenarios. In this paper, we propose a scenario-aware discriminator that captures scene-specific features and performs frequency-domain division, thereby enabling a more accurate quality assessment of the enhanced speech generated by the generator. We conducted comprehensive experiments on three representative models using two publicly available datasets. The results demonstrate that our method can effectively adapt to various generator architectures without altering their structure, thereby unlocking further performance gains in speech enhancement across different scenarios. |
| title | SaD: A Scenario-Aware Discriminator for Speech Enhancement |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.00405 |