论文标题
基于多种入学说法和SASV挑战的抽样策略2022，基于ASV的欺骗意识关注。
Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022
论文作者
论文摘要
目前的最新自动扬声器验证（ASV）系统很容易受到演示攻击的攻击，并且已经探索了将真正的对策与欺骗试验区分开来保护ASV。但是，ASV系统和CMS通常是独立开发和优化的，而无需考虑它们的相互关系。在本文中，我们提出了一个新的欺骗感知的ASV后端模块，该模块有效地根据说话者的相似性和CM分数有效地计算了ASV得分。除了两个分数的可学习融合功能外，所提出的后端模块还具有两种类型的注意力组件：缩放点和馈送前进的自我注意力，因此也可以同时学习多个注册话语的内部关系信息。此外，一种新的有效试验采样策略旨在模拟欺骗意识到的演讲者验证（SASV）挑战2022中引入的新的欺骗验证方案。
Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV. However, ASV systems and CMs are generally developed and optimized independently without considering their inter-relationship. In this paper, we propose a new spoofing-aware ASV back-end module that efficiently computes a combined ASV score based on speaker similarity and CM score. In addition to the learnable fusion function of the two scores, the proposed back-end module has two types of attention components, scaled-dot and feed-forward self-attention, so that intra-relationship information of multiple enrollment utterances can also be learned at the same time. Moreover, a new effective trials-sampling strategy is designed for simulating new spoofing-aware verification scenarios introduced in the Spoof-Aware Speaker Verification (SASV) challenge 2022.
