SA-SASV: An End-to-End Spoof-Aggregated Spoofing-Aware Speaker Verification System

Research in the past several years has boosted the performance of automatic speaker verification systems and countermeasure systems to deliver low Equal Error Rates (EERs) on each system. However, research on joint optimization of both systems is still limited. The SASV 2022 challenge was proposed to encourage the development of integrated spoofing aware speaker verification system (SASV) with new metrics to evaluate joint model performance. This paper proposes an ensemble-free end-to-end solution, SA-SASV, to build a SASV system with multi-task classifiers, which are optimized by multiple losses. The proposed system is evaluated on the ASVSpoof 2019 evaluation dataset and improves the performance of baseline systems from 8.76% to 1.81% in SASV-EER.