Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning
This addresses the scalability issue of manual annotation in speaker verification systems, though it is incremental as it builds on existing self-supervised and contrastive learning methods.
The paper tackles the problem of speaker verification without extensive labeled data by using self-supervised learning from raw audio, achieving competitive results and outperforming a supervised baseline when fine-tuned with a small amount of labeled data.
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.