  Unsupervised representation learning has shown remarkable achievement by
reducing the performance gap with supervised feature learning, especially in
the image domain. In this study, to extend the technique of unsupervised
learning to the speech domain, we propose the Momentum Contrast for VoxCeleb
(MoCoVox) as a form of learning mechanism. We pre-trained the MoCoVox on the
VoxCeleb1 by implementing instance discrimination. Applying MoCoVox for speaker
verification revealed that it outperforms the state-of-the-art metric
learning-based approach by a large margin. We also empirically demonstrate the
features of contrastive learning in the speech domain by analyzing the
distribution of learned representations. Furthermore, we explored which pretext
task is adequate for speaker verification. We expect that learning speaker
representation without human supervision helps to address the open-set speaker
recognition.

Audio and Speech Processing;Sound