June 5, 2024, 4:44 a.m. | Victor Miara, Theo Lepage, Reda Dehak

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02285v1 Announce Type: cross
Abstract: Recent advancements in Self-Supervised Learning (SSL) have shown promising results in Speaker Verification (SV). However, narrowing the performance gap with supervised systems remains an ongoing challenge. Several studies have observed that speech representations from large-scale ASR models contain valuable speaker information. This work explores the limitations of fine-tuning these models for SV using an SSL contrastive objective in an end-to-end approach. Then, we propose a framework to learn speaker representations in an SSL context by …

abstract arxiv asr challenge cs.lg cs.sd eess.as gap however information performance results scale self-supervised learning speaker speech ssl studies supervised learning systems type verification

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