Distillation-Based Feature Extraction Algorithm for Source Speaker Verification

Automatic speaker verification (ASV) systems face significant challenges when exposed to spoofing attacks, necessitating robust countermeasures. In this work, we focus on the source speaker verification (SSV) task, which aims to identify the source speaker hidden in spoofed speech generated by voice conversion (VC) systems. We propose a distillation-based feature extraction algorithm to enhance the model’s ability to verify source speakers. Our method employs a pretraining ASV model as a teacher network and the SSV model as a student network, using bona fide speech to guide the learning process. However, the improvements were marginal, particularly on the development set, indicating the complexity and resource demands of fine-tuning the distillation parameters. Our findings underscore the inherent difficulties in SSV and highlight the need for further research to develop more effective solutions. Besides, our submission won fourth place in the 2024 Source Speaker Tracking Challenge.