Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge
This addresses the problem of accurate meeting transcription for speech technology researchers, but it is incremental as it focuses on dataset release and challenge summarization.
The paper tackled the challenge of transcribing multi-party meetings by releasing a 120-hour Mandarin dataset and organizing two tracks for speaker diarization and multi-speaker ASR, summarizing results and techniques from submissions.
The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies. The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2). Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants' headset microphone. We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions.