Summary on the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge
Fan Yu, Shiliang Zhang, Pengcheng Guo, Yihui Fu, Zhihao Du, Siqi Zheng, Weilong Huang, Lei Xie
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 micro-phone 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.
BibTeX
@inproceedings{icassp2022_summaryontheicas,
title = {Summary on the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge},
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 Zheng-Hua Tan and DeLiang Wang and Yanmin Qian and Kong Aik Lee and Zhijie Yan and Bin Ma and Xin Xu and Hui Bu},
booktitle = {ICASSP 2022},
year = {2022}
}