Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification

arXiv:2306.03664v1 Announce Type: cross
Abstract: Most state-of-the-art self-supervised speaker verification systems rely on a contrastive-based objective function to learn speaker representations from unlabeled speech data. We explore different ways to improve the performance of these methods by: (1) revisiting how positive and negative pairs are sampled through a "symmetric" formulation of the contrastive loss; (2) introducing margins similar to AM-Softmax and AAM-Softmax that have been widely adopted in the supervised setting. We demonstrate the effectiveness of the symmetric contrastive loss …

abstract art arxiv cs.lg data eess.as explore function learn margins negative performance positive speaker speech state systems through type verification