  Speaker diarization is an essential step for processing multi-speaker audio.
Although an end-to-end neural diarization (EEND) method achieved
state-of-the-art performance, it is limited to a fixed number of speakers. In
this paper, we solve this fixed number of speaker issue by a novel speaker-wise
conditional inference method based on the probabilistic chain rule. In the
proposed method, each speaker's speech activity is regarded as a single random
variable, and is estimated sequentially conditioned on previously estimated
other speakers' speech activities. Similar to other sequence-to-sequence
models, the proposed method produces a variable number of speakers with a stop
sequence condition. We evaluated the proposed method on multi-speaker audio
recordings of a variable number of speakers. Experimental results show that the
proposed method can correctly produce diarization results with a variable
number of speakers and outperforms the state-of-the-art end-to-end speaker
diarization methods in terms of diarization error rate.
