  Recent advances in unsupervised speech representation learning discover new
approaches and provide new state-of-the-art for diverse types of speech
processing tasks. This paper presents an investigation of using wav2vec 2.0
deep speech representations for the speaker recognition task. The proposed
fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling
back-end using additive angular margin loss allows to obtain deep speaker
embedding extractor that is well-generalized across different domains. It is
concluded that Contrastive Predictive Coding pretraining scheme efficiently
utilizes the power of unlabeled data, and thus opens the door to powerful
transformer-based speaker recognition systems. The experimental results
obtained in this study demonstrate that fine-tuning can be done on relatively
small sets and a clean version of data. Using data augmentation during
fine-tuning provides additional performance gains in speaker verification. In
this study speaker recognition systems were analyzed on a wide range of
well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18
development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES
evaluation set, NIST 2021 SRE, and CTS challenges sets.
