@inproceedings{0ec72516f63448ac8da305381ace9f55,
title = "Summary on the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge",
abstract = "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.",
keywords = "Alimeeting, M2MeT, Meeting Transcription, Multi-speaker ASR, Speaker Diarization",
author = "Fan Yu and Shiliang Zhang and Pengcheng Guo and Yihui Fu and Zhihao Du and Siqi Zheng and Weilong Huang and Lei Xie and Tan, \{Zheng Hua\} and Wang, \{De Liang\} and Yanmin Qian and Lee, \{Kong Aik\} and Zhijie Yan and Bin Ma and Xin Xu and Hui Bu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
month = may,
doi = "10.1109/ICASSP43922.2022.9746270",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "9156--9160",
booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",
}