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Due to the scarcity of publicly available diarization data, the modelperformance can be improved by training a single model with data from differentdomains. In this work, we propose to incorporate domain information to train asingle end-to-end diarization model for multiple domains. First, we employdomain adaptive training with parameter-efficient adapters for on-the-fly modelreconfiguration. Second, we introduce an auxiliary domain classification taskto make the diarization model more domain-aware. For seen domains, thecombination of our proposed methods reduces the absolute DER from 17.66% to16.59% when compared with the baseline. During inference, adapters fromground-truth domains are not available for unseen domains. We demonstrate ourmodel exhibits a stronger generalizability to unseen domains when adapters areremoved. For two unseen domains, this improves the DER performance from 39.91%to 23.09% and 25.32% to 18.76% over the baseline, respectively.

Robust End-to-End Diarization with Domain Adaptive Training and Multi-Task Learning