  This paper investigates the utilization of an end-to-end diarization model as
post-processing of conventional clustering-based diarization. Clustering-based
diarization methods partition frames into clusters of the number of speakers;
thus, they typically cannot handle overlapping speech because each frame is
assigned to one speaker. On the other hand, some end-to-end diarization methods
can handle overlapping speech by treating the problem as multi-label
classification. Although some methods can treat a flexible number of speakers,
they do not perform well when the number of speakers is large. To compensate
for each other's weakness, we propose to use a two-speaker end-to-end
diarization method as post-processing of the results obtained by a
clustering-based method. We iteratively select two speakers from the results
and update the results of the two speakers to improve the overlapped region.
Experimental results show that the proposed algorithm consistently improved the
performance of the state-of-the-art methods across CALLHOME, AMI, and DIHARD II
datasets.
