论文标题
SASV基于预先训练的ASV系统和集成评分模块
SASV Based on Pre-trained ASV System and Integrated Scoring Module
论文作者
论文摘要
Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge.当前SASV系统中ASV和反欺骗对策（CM）的训练和评分相对独立，忽略了相关性。在本文中，通过利用这两个任务之间的相关性，可以通过简单地基于预先训练的ASV子系统训练几层层来获得集成的SASV系统。预训练的ASV系统中的功能用于逻辑访问欺骗语音检测。此外，使用预训练的ASV系统提取的扬声器嵌入用于改善CM的性能。集成的评分模块将ASV和抗烟分支的嵌入为输入，并通过矩阵操作保留两个任务之间的相关性，以产生集成的SASV分数。在SASV 2022挑战的开发数据集中，提交的主要系统达到了相等的错误率（EER）为3.07 \％，评估零件的4.30 \％\％，比基线系统相比有25 \％的改善。
Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge. The training and scoring of ASV and anti-spoofing countermeasure (CM) in current SASV systems are relatively independent, ignoring the correlation. In this paper, by leveraging the correlation between the two tasks, an integrated SASV system can be obtained by simply training a few more layers on the basis of the baseline pre-trained ASV subsystem. The features in pre-trained ASV system are utilized for logical access spoofing speech detection. Further, speaker embeddings extracted by the pre-trained ASV system are used to improve the performance of the CM. The integrated scoring module takes the embeddings of the ASV and anti-spoofing branches as input and preserves the correlation between the two tasks through matrix operations to produce integrated SASV scores. Submitted primary system achieved equal error rate (EER) of 3.07\% on the development dataset of the SASV 2022 Challenge and 4.30\% on the evaluation part, which is a 25\% improvement over the baseline systems.
