Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR

Kai Tan; Lin Zhang; Ruiteng Zhang; Johan Rohdin; Leibny Paola García-Perera; Zexin Cai; Sanjeev Khudanpur; Matthew Wiesner; Nicholas Andrews

arXiv · 2603.13780

Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR

Kai Tan·Lin Zhang·Ruiteng Zhang·Johan Rohdin·Leibny Paola García-Perera·Zexin Cai·Sanjeev Khudanpur·Matthew Wiesner·Nicholas Andrews

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Abstract

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.

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BibTeX RIS

Kai Tan, Lin Zhang, Ruiteng Zhang, Johan Rohdin, Leibny Paola García-Perera, Zexin Cai, Sanjeev Khudanpur, Matthew Wiesner, Nicholas Andrews. 2026-03-14. Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR. https://arxiv.org/abs/2603.13780

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