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

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.

Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge

“…Noise augmentation, reverberation simulation, speed perturbation and SpecAugmentation are the mainstream methods with stable performance improvement. According to the report provided by second-place team B24 [55], relative CER reduction of 13.5% can be achieved by multi-channel multi-speaker data simulation as compared with the baseline trained using Train-Ali-far. Compared with speaker diarization, data simulation for multi-speaker ASR is more complex, which needs to consider various factors such as speaker turn and conversation duration.…”