论文标题
优化串联扬声器验证和反欺骗系统
Optimizing Tandem Speaker Verification and Anti-Spoofing Systems
论文作者
论文摘要
由于自动扬声器验证（ASV）系统容易受到欺骗攻击的影响，因此它们通常与欺骗对策（CM）系统一起使用以提高安全性。例如，CM可以首先确定输入是否为人类语音，然后ASV可以确定此演讲是否与说话者的身份相匹配。可以通过串联检测成本函数（T-DCF）来测量这种串联系统的性能。但是，通常使用不同的指标和数据分别对ASV和CM系统分别训练，这不会优化其合并性能。在这项工作中，我们建议通过创建可区分版本的T-DCF并采用增强学习技术来直接优化串联系统。结果表明，这些方法比Finetuning提供了更好的结果，而我们的方法在约束设置中提供了20％的ASVSPOOF19数据集中T-DCF的相对相对改善。
As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are typically used in conjunction with spoofing countermeasure (CM) systems to improve security. For example, the CM can first determine whether the input is human speech, then the ASV can determine whether this speech matches the speaker's identity. The performance of such a tandem system can be measured with a tandem detection cost function (t-DCF). However, ASV and CM systems are usually trained separately, using different metrics and data, which does not optimize their combined performance. In this work, we propose to optimize the tandem system directly by creating a differentiable version of t-DCF and employing techniques from reinforcement learning. The results indicate that these approaches offer better outcomes than finetuning, with our method providing a 20% relative improvement in the t-DCF in the ASVSpoof19 dataset in a constrained setting.
