  Recent development of speech processing, such as speech recognition, speaker
diarization, etc., has inspired numerous applications of speech technologies.
The meeting scenario is one of the most valuable and, at the same time, most
challenging scenarios for the deployment of speech technologies. Specifically,
two typical tasks, speaker diarization and multi-speaker automatic speech
recognition have attracted much attention recently. However, the lack of large
public meeting data has been a major obstacle for the advancement of the field.
Therefore, we make available the AliMeeting corpus, which consists of 120 hours
of recorded Mandarin meeting data, including far-field data collected by
8-channel microphone array as well as near-field data collected by headset
microphone. Each meeting session is composed of 2-4 speakers with different
speaker overlap ratio, recorded in rooms with different size. Along with the
dataset, we launch the ICASSP 2022 Multi-channel Multi-party Meeting
Transcription Challenge (M2MeT) with two tracks, namely speaker diarization and
multi-speaker ASR, aiming to provide a common testbed for meeting rich
transcription and promote reproducible research in this field. In this paper we
provide a detailed introduction of the AliMeeting dateset, challenge rules,
evaluation methods and baseline systems.
