  Speaker-attributed automatic speech recognition (SA-ASR) in multi-party
meeting scenarios is one of the most valuable and challenging ASR task. It was
shown that single-channel frame-level diarization with serialized output
training (SC-FD-SOT), single-channel word-level diarization with SOT
(SC-WD-SOT) and joint training of single-channel target-speaker separation and
ASR (SC-TS-ASR) can be exploited to partially solve this problem. In this
paper, we propose three corresponding multichannel (MC) SA-ASR approaches,
namely MC-FD-SOT, MC-WD-SOT and MC-TS-ASR. For different tasks/models,
different multichannel data fusion strategies are considered, including
channel-level cross-channel attention for MC-FD-SOT, frame-level cross-channel
attention for MC-WD-SOT and neural beamforming for MC-TS-ASR. Results on the
AliMeeting corpus reveal that our proposed models can consistently outperform
the corresponding single-channel counterparts in terms of the speaker-dependent
character error rate.
