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 fine-tuning a pre-trained WavLM with a supervised loss using
pseudo-labels. Initial pseudo-labels are derived from an SSL DINO-based model
and are iteratively refined by clustering the model embeddings. Our method
achieves 0.99% EER on VoxCeleb1-O, establishing the new state-of-the-art on
self-supervised SV. As this performance is close to our supervised baseline of
0.94% EER, this contribution is a step towards supervised performance on SV
with SSL.

自我监督学习在发音验证中表现出与有监督系统相近的性能，本研究通过对预训练的 WavLM 进行自我监督有监督微调并使用伪标签，实现了语音表示学习中的有监督性能，取得了 0.99％的 EER，接近有监督基线 0.94％的 EER。