Continual Self-Supervised Domain Adaptation for End-to-End Speaker Diarization

In conventional domain adaptation for speaker diarization, a large collection of annotated conversations from the target domain is required. In this work, we propose a novel continual training scheme for domain adaptation of an end-to-end speaker diarization system, which processes one conversation at a time and benefits from full self-supervision thanks to pseudo-labels. The qualities of our method allow for autonomous adaptation (e.g. of a voice assistant to a new house-hold), while also avoiding permanent storage of possibly sensitive user conversations. We experiment extensively on the 11 domains of the DIHARD III corpus and show the effectiveness of our approach with respect to a pre-trained base-line, achieving a relative 17% performance improvement. We also find that data augmentation and a well-defined target domain are key factors to avoid divergence and to benefit from transfer.