  State-of-the-art speaker verification systems are inherently dependent on
some kind of human supervision as they are trained on massive amounts of
labeled data. However, manually annotating utterances is slow, expensive and
not scalable to the amount of data available today. In this study, we explore
self-supervised learning for speaker verification by learning representations
directly from raw audio. The objective is to produce robust speaker embeddings
that have small intra-speaker and large inter-speaker variance. Our approach is
based on recent information maximization learning frameworks and an intensive
data augmentation pre-processing step. We evaluate the ability of these methods
to work without contrastive samples before showing that they achieve better
performance when combined with a contrastive loss. Furthermore, we conduct
experiments to show that our method reaches competitive results compared to
existing techniques and can get better performances compared to a supervised
baseline when fine-tuned with a small portion of labeled data.
