Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911524108369920 |
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| author | Tan, Kai Zhang, Lin Zhang, Ruiteng Rohdin, Johan García-Perera, Leibny Paola Cai, Zexin Khudanpur, Sanjeev Wiesner, Matthew Andrews, Nicholas |
| author_facet | Tan, Kai Zhang, Lin Zhang, Ruiteng Rohdin, Johan García-Perera, Leibny Paola Cai, Zexin Khudanpur, Sanjeev Wiesner, Matthew Andrews, Nicholas |
| contents | Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent ASV and CM scores. To better modeling SASV, some frameworks integrate ASV and CM within a single network. However, these solutions are typically bi-encoder based, offer limited interpretability, and cannot be readily adapted to new evaluation parameters without retraining. Based on this, we propose a unified end-to-end framework via a three-class formulation that enables log-likelihood ratio (LLR) inference from class logits for a more interpretable decision pipeline. Experiments show comparable performance to existing methods on ASVSpoof5 and better results on SpoofCeleb. The visualization and analysis also prove that the three-class reformulation provides more interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13780 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR Tan, Kai Zhang, Lin Zhang, Ruiteng Rohdin, Johan García-Perera, Leibny Paola Cai, Zexin Khudanpur, Sanjeev Wiesner, Matthew Andrews, Nicholas Audio and Speech Processing Sound Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent ASV and CM scores. To better modeling SASV, some frameworks integrate ASV and CM within a single network. However, these solutions are typically bi-encoder based, offer limited interpretability, and cannot be readily adapted to new evaluation parameters without retraining. Based on this, we propose a unified end-to-end framework via a three-class formulation that enables log-likelihood ratio (LLR) inference from class logits for a more interpretable decision pipeline. Experiments show comparable performance to existing methods on ASVSpoof5 and better results on SpoofCeleb. The visualization and analysis also prove that the three-class reformulation provides more interpretability. |
| title | Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2603.13780 |