The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge

• URL: http://arxiv.org/abs/2202.04261v2
• Date: Thu, 10 Feb 2022 02:58:07 GMT
• Title: The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
• Authors: Chen Shen, Yi Liu, Wenzhi Fan, Bin Wang, Shixue Wen, Yao Tian, Jun Zhang, Jingsheng Yang, Zejun Ma
• Abstract summary: 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. For Track 2, we develop our system using the Conformer model in a joint CTC-attention architecture.
• Score: 18.33054364289739
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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