论文标题
SASV挑战融合方法的比较研究2022
A Comparative Study of Fusion Methods for SASV Challenge 2022
论文作者
论文摘要
自动扬声器验证（ASV）系统是一种生物措施身份验证。它可以受到入侵者的攻击，他伪造数据以获取受保护的信息。对策（CM）是检测这些欺骗攻击的特殊算法。尽管ASVSPOOF挑战系列集中在固定ASV系统的CM开发上，但新的欺骗意识扬声器验证（SASV）挑战组织者认为，如果共同优化了CM和ASV系统，则可以实现最佳结果。合作优化的方法之一是对从ASV和CM模型获得的嵌入或分数进行融合。 SASV挑战2022的基线提出了两种类型的融合：带3层MLP的得分和后端合奏。本文介绍了我们对其他融合方法的研究，包括提高嵌入方式，而嵌入方式以前尚未用于反欺骗研究。
Automatic Speaker Verification (ASV) system is a type of bio-metric authentication. It can be attacked by an intruder, who falsifies data in order to get access to protected information. Countermeasures (CM) are special algorithms that detect these spoofing-attacks. While the ASVspoof Challenge series were focused on the development of CM for fixed ASV system, the new Spoofing Aware Speaker Verification (SASV) Challenge organizers believe that best results can be achieved if CM and ASV systems are optimized jointly. One of the approaches for cooperative optimization is a fusion over embeddings or scores obtained from ASV and CM models. The baselines of SASV Challenge 2022 present two types of fusion: score-sum and back-end ensemble with a 3-layer MLP. This paper describes our research of other fusion methods, including boosting over embeddings, which has not been used in anti-spoofing studies before.
