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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21833 |
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| _version_ | 1866915515899838464 |
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| author | Zhao, Siyi Wang, Wei Qian, Yanmin |
| author_facet | Zhao, Siyi Wang, Wei Qian, Yanmin |
| contents | Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-band pruning. Frame resampling downsamples inputs within layers, utilizing residual connections to mitigate information loss. Simultaneously, sub-band pruning progressively excludes less informative frequency bands, further reducing computational demands. Extensive experiments on synthetic and real-world noisy datasets demonstrate that our system reduces SE computational overhead over 66 compared to the standard BSRNN, while maintaining strong ASR performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning Zhao, Siyi Wang, Wei Qian, Yanmin Sound Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-band pruning. Frame resampling downsamples inputs within layers, utilizing residual connections to mitigate information loss. Simultaneously, sub-band pruning progressively excludes less informative frequency bands, further reducing computational demands. Extensive experiments on synthetic and real-world noisy datasets demonstrate that our system reduces SE computational overhead over 66 compared to the standard BSRNN, while maintaining strong ASR performance. |
| title | Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning |
| topic | Sound |
| url | https://arxiv.org/abs/2509.21833 |