Improving Short Utterance Anti-Spoofing with AASIST2
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913185097842688 |
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| author | Zhang, Yuxiang Lu, Jingze Shang, Zengqiang Wang, Wenchao Zhang, Pengyuan |
| author_facet | Zhang, Yuxiang Lu, Jingze Shang, Zengqiang Wang, Wenchao Zhang, Pengyuan |
| contents | The wav2vec 2.0 and integrated spectro-temporal graph attention network (AASIST) based countermeasure achieves great performance in speech anti-spoofing. However, current spoof speech detection systems have fixed training and evaluation durations, while the performance degrades significantly during short utterance evaluation. To solve this problem, AASIST can be improved to AASIST2 by modifying the residual blocks to Res2Net blocks. The modified Res2Net blocks can extract multi-scale features and improve the detection performance for speech of different durations, thus improving the short utterance evaluation performance. On the other hand, adaptive large margin fine-tuning (ALMFT) has achieved performance improvement in short utterance speaker verification. Therefore, we apply Dynamic Chunk Size (DCS) and ALMFT training strategies in speech anti-spoofing to further improve the performance of short utterance evaluation. Experiments demonstrate that the proposed AASIST2 improves the performance of short utterance evaluation while maintaining the performance of regular evaluation on different datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_08279 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Short Utterance Anti-Spoofing with AASIST2 Zhang, Yuxiang Lu, Jingze Shang, Zengqiang Wang, Wenchao Zhang, Pengyuan Audio and Speech Processing Sound The wav2vec 2.0 and integrated spectro-temporal graph attention network (AASIST) based countermeasure achieves great performance in speech anti-spoofing. However, current spoof speech detection systems have fixed training and evaluation durations, while the performance degrades significantly during short utterance evaluation. To solve this problem, AASIST can be improved to AASIST2 by modifying the residual blocks to Res2Net blocks. The modified Res2Net blocks can extract multi-scale features and improve the detection performance for speech of different durations, thus improving the short utterance evaluation performance. On the other hand, adaptive large margin fine-tuning (ALMFT) has achieved performance improvement in short utterance speaker verification. Therefore, we apply Dynamic Chunk Size (DCS) and ALMFT training strategies in speech anti-spoofing to further improve the performance of short utterance evaluation. Experiments demonstrate that the proposed AASIST2 improves the performance of short utterance evaluation while maintaining the performance of regular evaluation on different datasets. |
| title | Improving Short Utterance Anti-Spoofing with AASIST2 |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2309.08279 |