End-to-End Neural Diarization:Reformulating Speaker Diarization asSimple Multi-label Classification
TABLE III: DERs (%) on the CALLHOME with and without domain adaptation.
['[EMPTY]', 'w/o adaptation', 'with adaptatation']
The EEND models trained with simulated training set were overfitted to the specific overlap ratio of the training set. We expected that the overfitting would be mitigated by using domain adaptation. As expected, the domain adaptation significantly reduced the DER; our system thus achieved even better results than those of the x-vector-based system.