The Royalflush System of Speech Recognition for M2met Challenge

This paper describes our RoyalFlush system for the track of multi-speaker automatic speech recognition (ASR) in the M2MeT challenge. We adopted the serialized output training (SOT) based multi-speakers ASR system with large-scale simulation data. Firstly, we investigated a set of front-end methods, including multi-channel weighted predicted error (WPE), beamforming, speech separation, speech enhancement, etc., to process training, evaluation, and test sets. However, according to their experimental results, we only selected the WPE and beamforming approach as our front-end methods. Secondly, we made great efforts in the data augmentation for multi-speaker ASR, including adding noise and reverberation, over-lapped speech simulation, multi-channel speech simulation, speed perturbation, front-end processing, etc., which brought us a significant performance improvement. Finally, to make full use of the performance complementary of different model architecture, we trained the standard conformer based joint CTC/Attention (Conformer) and U2++ ASR model with a bidirectional attention decoder, a modification of Conformer, to fuse their results. Compared with the official baseline system, our system got a 12.22% absolute Character Error Rate (CER) reduction on the evaluation set and 12.11% on the test set.