The DKU-OPPO System for the 2022 Spoofing-Aware Speaker Verification Challenge
Xingming Wang, Xiaoyi Qin, Yikang Wang, Yunfei Xu, Ming Li · Interspeech 2022 · 2022
This paper describes our DKU-OPPO system for the 2022 Spoofing-Aware Speaker Verification (SASV) Challenge.First, we split the joint task into speaker verification (SV) and spoofing countermeasure (CM), these two tasks which are optimized separately.For ASV systems, four state-of-the-art methods are employed.For CM systems, we propose two methods on top of the challenge baseline to further improve the performance, namely Embedding Random Sampling Augmentation (ERSA) and One-Class Confusion Loss(OCCL).Second, we also explore whether SV embedding could help improve CM system performance.We observe a dramatic performance degradation of existing CM systems on the domain-mismatched Voxceleb2 dataset.Third, we compare different fusion strategies, including parallel score fusion and sequential cascaded systems.Compared to the 1.71% SASV-EER baseline, our submitted cascaded system obtains a 0.21% SASV-EER on the challenge official evaluation set.