Curriculum learning for self-supervised speaker verification

Understand

The goal of this paper is to train effective self-supervised speaker representations without identity labels.

• We propose two curriculum learning strategies within a self-supervised learning framework.
• The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset.
• The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds.