End-to-End Neural Diarization:Reformulating Speaker Diarization asSimple Multi-label Classification
TABLE V: DERs (%) with different number of heads. The models are trained with SimBeta2.
['Num. heads', 'Simulated  [ITALIC] β=2', 'Simulated  [ITALIC] β=3', 'Simulated  [ITALIC] β=5', 'Real CH', 'Real CSJ']
Here, performance improved as a result of increasing the number of heads. These results suggest that the SA-EEND models were trained to separate speakers via the global speaker characteristics represented by different heads, the required number of heads was at least the number of speakers, and more heads boosted performance.