Self-Supervised Learning Based Domain Adaptation for Robust Speaker Verification
Self-Supervised Learning Based Domain Adaptation for Robust Speaker Verification
Large performance degradation is often observed for speaker verification systems when applied to a new domain dataset. Given an unlabeled target-domain dataset, unsupervised domain adaptation (UDA) methods, which usually leverage adversarial training strategies, are commonly used to bridge the performance gap caused by the domain mismatch. However, such adversarial training …