Cross-Domain ArcFace:Learnging Robust Speaker Representation Under the Far-Field Speaker Verification

The system of speaker verification system shows outstanding performance with the assistance of different types of loss functions with angular margin penalty, which can enforce the intra-class compactness and inter-class discrepancy. However, the power of classification may degrade largely when encountering the cross-domain problems, especially in far-field scenes. Thus, we propose a novel Cross-Domain ArcFace(CD-ArcFace) loss function. By adopting distinct margin penalty in different domain when conducting mix-data fine-tuning, the performance of various speaker verification system can be further improved. This experiment is carried on FFSVC2022. The final score level of our fusion system for the task1 achieves 4.028% and 4.368% EER on the development set and evaluation set.