Post-training for Deepfake Speech Detection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914104732549120 |
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| author | Ge, Wanying Wang, Xin Liu, Xuechen Yamagishi, Junichi |
| author_facet | Ge, Wanying Wang, Xin Liu, Xuechen Yamagishi, Junichi |
| contents | We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained models already exhibit strong robustness and generalization to unseen deepfake speech. When they are further fine-tuned on the Deepfake-Eval-2024 dataset, these models consistently surpass existing state-of-the-art detectors that do not leverage post-training. Model checkpoints and source code are available online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Post-training for Deepfake Speech Detection Ge, Wanying Wang, Xin Liu, Xuechen Yamagishi, Junichi Audio and Speech Processing We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained models already exhibit strong robustness and generalization to unseen deepfake speech. When they are further fine-tuned on the Deepfake-Eval-2024 dataset, these models consistently surpass existing state-of-the-art detectors that do not leverage post-training. Model checkpoints and source code are available online. |
| title | Post-training for Deepfake Speech Detection |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.21090 |