CDMA: Cross-Domain Distance Metric Adaptation for Speaker Verification

Abstract

To solve the domain shift problem in speaker verification, one effective domain adaptation approach is to learn domain-invariant embeddings via aligning the source and target distributions in the embedding space. However, this approach could be problematic when the source and target domains are from the disjoint speaker label spaces as the embedding distributions of different speakers cannot be aligned. In this paper, we propose a Cross-domain Distance Metric Adaptation (CDMA) approach to alleviate the domain shift in the distance metric space, where the source and target domains share the same classes, i.e., within- and between-speaker. Specifically, the two target pairwise distance distributions are aligned with the source pairwise distance distributions and further separated to learn a domain-invariant metric, which is more suitable for speaker verification based on metric learning. Experiments indicate that CDMA significantly outperforms the approach proposed in the embedding space.