  In this paper, we present a conditional multitask learning method for
end-to-end neural speaker diarization (EEND). The EEND system has shown
promising performance compared with traditional clustering-based methods,
especially in the case of overlapping speech. In this paper, to further improve
the performance of the EEND system, we propose a novel multitask learning
framework that solves speaker diarization and a desired subtask while
explicitly considering the task dependency. We optimize speaker diarization
conditioned on speech activity and overlap detection that are subtasks of
speaker diarization, based on the probabilistic chain rule. Experimental
results show that our proposed method can leverage a subtask to effectively
model speaker diarization, and outperforms conventional EEND systems in terms
of diarization error rate.
