This paper describes our submission to ICASSP 2022 Multi-channel Multi-party
Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several
approaches to empower the clustering-based speaker diarization system to handle
overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA)
estimation are used to improve the accuracy of speaker diarization.
Multi-channel combination and overlap detection are applied to reduce the
missed speaker error. A modified DOVER-Lap is also proposed to fuse the results
of different systems. We achieve the final DER of 5.79% on the Eval set and
7.23% on the Test set. For Track 2, we develop our system using the Conformer
model in a joint CTC-attention architecture. Serialized output training is
adopted to multi-speaker overlapped speech recognition. We propose a neural
front-end module to model multi-channel audio and train the model end-to-end.
Various data augmentation methods are utilized to mitigate over-fitting in the
multi-channel multi-speaker E2E system. Transformer language model fusion is
developed to achieve better performance. The final CER is 19.2% on the Eval set
and 20.8% on the Test set.

本文介绍了我们在 ICASSP 2022 M2MeT 挑战中的参赛作品。对于 Track 1，我们提出多种方法来加强聚类式说话人分割系统以应对重叠语音；对于 Track 2，我们采用 Conformer 模型和神经前端模块来训练系统，实现多声道混叠语音识别，最终取得了比较好的性能表现。