  Training speaker-discriminative and robust speaker verification systems
without explicit speaker labels remains a persisting challenge. In this paper,
we propose a new self-supervised speaker verification approach,
Self-Distillation Prototypes Network (SDPN), which effectively facilitates
self-supervised speaker representation learning. SDPN assigns the
representation of the augmented views of an utterance to the same prototypes as
the representation of the original view, thereby enabling effective knowledge
transfer between the views. Originally, due to the lack of negative pairs in
the SDPN training process, the network tends to align positive pairs very
closely in the embedding space, a phenomenon known as model collapse. To
alleviate this problem, we introduce a diversity regularization term to
embeddings in SDPN. Comprehensive experiments on the VoxCeleb datasets
demonstrate the superiority of SDPN in self-supervised speaker verification.
SDPN sets a new state-of-the-art on the VoxCeleb1 speaker verification
evaluation benchmark, achieving Equal Error Rate 1.80%, 1.99%, and 3.62% for
trial VoxCeleb1-O, VoxCeleb1-E and VoxCeleb1-H respectively, without using any
speaker labels in training. Ablation studies show that both proposed learnable
prototypes in self-distillation network and diversity regularization contribute
to the verification performance.
