  This study describes our system for Task 1 Single-speaker Visual Speech
Recognition (VSR) fixed track in the Chinese Continuous Visual Speech
Recognition Challenge (CNVSRC) 2023. Specifically, we use intermediate
connectionist temporal classification (Inter CTC) residual modules to relax the
conditional independence assumption of CTC in our model. Then we use a
bi-transformer decoder to enable the model to capture both past and future
contextual information. In addition, we use Chinese characters as the modeling
units to improve the recognition accuracy of our model. Finally, we use a
recurrent neural network language model (RNNLM) for shallow fusion in the
inference stage. Experiments show that our system achieves a character error
rate (CER) of 38.09% on the Eval set which reaches a relative CER reduction of
21.63% over the official baseline, and obtains a second place in the challenge.
