Self Supervised Speaker Verification

Self-supervised speaker verification aims to build speaker recognition systems without relying on manually labeled data, a significant challenge in the field. Current research focuses on developing novel self-supervised learning frameworks, often employing contrastive learning, self-distillation, or siamese networks, and incorporating techniques like prototype learning and regularization to improve the robustness and discriminative power of learned speaker embeddings. These advancements are crucial for reducing the reliance on expensive and time-consuming data annotation, potentially leading to more efficient and scalable speaker verification systems for various applications.