  Training speaker-discriminative and robust speaker verification systems
without speaker labels is still challenging and worthwhile to explore. In this
study, we propose an effective self-supervised learning framework and a novel
regularization strategy to facilitate self-supervised speaker representation
learning. Different from contrastive learning-based self-supervised learning
methods, the proposed self-supervised regularization (SSReg) focuses
exclusively on the similarity between the latent representations of positive
data pairs. We also explore the effectiveness of alternative online data
augmentation strategies on both the time domain and frequency domain. With our
strong online data augmentation strategy, the proposed SSReg shows the
potential of self-supervised learning without using negative pairs and it can
significantly improve the performance of self-supervised speaker representation
learning with a simple Siamese network architecture. Comprehensive experiments
on the VoxCeleb datasets demonstrate that our proposed self-supervised approach
obtains a 23.4% relative improvement by adding the effective self-supervised
regularization and outperforms other previous works.
