  Due to the high performance of multi-channel speech processing, we can use
the outputs from a multi-channel model as teacher labels when training a
single-channel model with knowledge distillation. To the contrary, it is also
known that single-channel speech data can benefit multi-channel models by
mixing it with multi-channel speech data during training or by using it for
model pretraining. This paper focuses on speaker diarization and proposes to
conduct the above bi-directional knowledge transfer alternately. We first
introduce an end-to-end neural diarization model that can handle both single-
and multi-channel inputs. Using this model, we alternately conduct i) knowledge
distillation from a multi-channel model to a single-channel model and ii)
finetuning from the distilled single-channel model to a multi-channel model.
Experimental results on two-speaker data show that the proposed method mutually
improved single- and multi-channel speaker diarization performances.
