  Due to the scarcity of publicly available diarization data, the model
performance can be improved by training a single model with data from different
domains. In this work, we propose to incorporate domain information to train a
single end-to-end diarization model for multiple domains. First, we employ
domain adaptive training with parameter-efficient adapters for on-the-fly model
reconfiguration. Second, we introduce an auxiliary domain classification task
to make the diarization model more domain-aware. For seen domains, the
combination of our proposed methods reduces the absolute DER from 17.66% to
16.59% when compared with the baseline. During inference, adapters from
ground-truth domains are not available for unseen domains. We demonstrate our
model exhibits a stronger generalizability to unseen domains when adapters are
removed. 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.
