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arXiv · 2110.07393

M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge

Fan Yu·Shiliang Zhang·Yihui Fu·Lei Xie·Siqi Zheng·Zhihao Du·Weilong Huang·Pengcheng Guo·Zhijie Yan·Bin Ma·Xin Xu·Hui Bu
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Abstract

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.

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Fan Yu, Shiliang Zhang, Yihui Fu, Lei Xie, Siqi Zheng, Zhihao Du, Weilong Huang, Pengcheng Guo, Zhijie Yan, Bin Ma, Xin Xu, Hui Bu. 2021-10-14. M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge. https://arxiv.org/abs/2110.07393

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