Pushing the limits of self-supervised speaker verification using regularized distillation framework

Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng, Qian Chen

Submitted on 8 November 2022


Training robust speaker verification systems without speaker labels has long been a challenging task. Previous studies observed a large performance gap between self-supervised and fully supervised methods. In this paper, we apply a non-contrastive self-supervised learning framework called DIstillation with NO labels (DINO) and propose two regularization terms applied to embeddings in DINO. One regularization term guarantees the diversity of the embeddings, while the other regularization term decorrelates the variables of each embedding. The effectiveness of various data augmentation techniques are explored, on both time and frequency domain. A range of experiments conducted on the VoxCeleb datasets demonstrate the superiority of the regularized DINO framework in speaker verification. Our method achieves the state-of-the-art speaker verification performance under a single-stage self-supervised setting on VoxCeleb. The codes will be made publicly-available.


Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing; Computer Science - Sound