  In this paper, we propose self-supervised speaker representation learning
strategies, which comprise of a bootstrap equilibrium speaker representation
learning in the front-end and an uncertainty-aware probabilistic speaker
embedding training in the back-end. In the front-end stage, we learn the
speaker representations via the bootstrap training scheme with the uniformity
regularization term. In the back-end stage, the probabilistic speaker
embeddings are estimated by maximizing the mutual likelihood score between the
speech samples belonging to the same speaker, which provide not only speaker
representations but also data uncertainty. Experimental results show that the
proposed bootstrap equilibrium training strategy can effectively help learn the
speaker representations and outperforms the conventional methods based on
contrastive learning. Also, we demonstrate that the integrated two-stage
framework further improves the speaker verification performance on the
VoxCeleb1 test set in terms of EER and MinDCF.
