  In recent years, self-supervised learning paradigm has received extensive
attention due to its great success in various down-stream tasks. However, the
fine-tuning strategies for adapting those pre-trained models to speaker
verification task have yet to be fully explored. In this paper, we analyze
several feature extraction approaches built on top of a pre-trained model, as
well as regularization and learning rate schedule to stabilize the fine-tuning
process and further boost performance: multi-head factorized attentive pooling
is proposed to factorize the comparison of speaker representations into
multiple phonetic clusters. We regularize towards the parameters of the
pre-trained model and we set different learning rates for each layer of the
pre-trained model during fine-tuning. The experimental results show our method
can significantly shorten the training time to 4 hours and achieve SOTA
performance: 0.59%, 0.79% and 1.77% EER on Vox1-O, Vox1-E and Vox1-H,
respectively.
