  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 and so on, to process training,
validation and test sets. But we only selected WPE and beamforming as our
frontend methods according to their experimental results. Secondly, we made
great efforts in the data augmentation for multi-speaker ASR, mainly including
adding noise and reverberation, overlapped speech simulation, multi-channel
speech simulation, speed perturbation, front-end processing, and so on, which
brought us a great performance improvement. Finally, in order 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. Comparing with the official baseline system, our system got a
12.22% absolute Character Error Rate (CER) reduction on the validation set and
12.11% on the test set.
