  Recent progress on end-to-end neural diarization (EEND) has enabled
overlap-aware speaker diarization with a single neural network. This paper
proposes to enhance EEND by using multi-channel signals from distributed
microphones. We replace Transformer encoders in EEND with two types of encoders
that process a multi-channel input: spatio-temporal and co-attention encoders.
Both are independent of the number and geometry of microphones and suitable for
distributed microphone settings. We also propose a model adaptation method
using only single-channel recordings. With simulated and real-recorded
datasets, we demonstrated that the proposed method outperformed conventional
EEND when a multi-channel input was given while maintaining comparable
performance with a single-channel input. We also showed that the proposed
method performed well even when spatial information is inoperative given
multi-channel inputs, such as in hybrid meetings in which the utterances of
multiple remote participants are played back from the same loudspeaker.
