Background & Task Overview

Recent development of speech signal 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 speech technologies. Because such scenarios have free speaking styles and complex acoustic conditions such as overlapping speech, unknown number of speakers, far-field signals in large conference rooms, noise and reverberation etc.

However, the lack of large public real meeting data has been a major obstacle for advancement of the field. Since meeting transcription involves numerous related processing components, more informa- tion have to be carefully collected and labelled, such as speaker identity, speech context, onset/offset time, etc. All these information require precise and accurate annotations, which is expensive and time- consuming. Although several relevant datasets have been released, most of them suffer from various limitations, ranging from corpus setup such as corpus size, number of speakers, variety of spatial loca- tions relative to the microphone arrays, collection condition, etc., to corpus content such as recording quality, accented speech, speaking style, etc. Moreover, almost all public available meeting corpora are collected in English, and the differences among different languages limit the development of Mandarin meeting transcription.

Therefore, we release the AliMeeting corpus, which consists of 120 hours of real recorded Mandarin meeting data, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants’ headset microphone. Moreover, we will launch the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT), as an ICASSP2022 Signal Processing Grand Challenge. The challenge consists of two tracks, namely speaker diarization and multi-speaker ASR. We provide a detailed introduction of the dateset, rules, evaluation methods and baseline systems, aiming to further promote reproducible research in this field. For details, please also refer to the paper we have already published: M2MET paper https://arxiv.org/abs/2110.07393

We will provide the code of the baseline system for speech recognition and speaker diarization in conference scenario as a reference. The goal is to simplify the training and evaluation procedures, so that participants can easily and flexibly experiment and verify the neural network-based method.GitHub link: https://github.com/yufan-aslp/AliMeeting

All teams need to submit a system description paper along with the submitted results on the final test set. The organizer will select papers with high system ranking and technical quality and include them in the ICASSP2022 Proceedings.

More details:https://www.alibabacloud.com/m2met-alimeeting