论文标题
欺骗意识的扬声器验证的概率融合框架
A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification
论文作者
论文摘要
自动扬声器验证（ASV）系统的性能可能会因欺骗攻击而降低。大多数现有的作品旨在开发独立的欺骗对策（CM）系统。相对较少的工作针对开发集成的欺骗意识扬声器验证（SASV）系统。在最近的SASV挑战中，组织者通过释放官方协议和基准来鼓励建立这种整合。在本文中，我们构建了一个概率框架，用于融合ASV和CM子系统得分。我们进一步提出了直接推理和微调的融合策略，以根据框架预测SASV得分。令人惊讶的是，这些策略在SASV挑战的官方评估试验中，SASV相等的错误率（EER）从基准的19.31％显着提高了。我们通过消融研究来验证我们提出的组件的有效性，并通过分数分析提供见解。
The performance of automatic speaker verification (ASV) systems could be degraded by voice spoofing attacks. Most existing works aimed to develop standalone spoofing countermeasure (CM) systems. Relatively little work targeted at developing an integrated spoofing aware speaker verification (SASV) system. In the recent SASV challenge, the organizers encourage the development of such integration by releasing official protocols and baselines. In this paper, we build a probabilistic framework for fusing the ASV and CM subsystem scores. We further propose fusion strategies for direct inference and fine-tuning to predict the SASV score based on the framework. Surprisingly, these strategies significantly improve the SASV equal error rate (EER) from 19.31% of the baseline to 1.53% on the official evaluation trials of the SASV challenge. We verify the effectiveness of our proposed components through ablation studies and provide insights with score distribution analysis.
