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.

这项研究描述了我们在 2023 年中国持续视觉语音识别挑战赛中针对任务 1 单说话者视觉语音识别（VSR）固定轨道的系统。具体来说，我们在模型中使用中间连接主义时间分类（Inter CTC）残差模块来放宽 CTC 的条件独立性假设，然后使用双变压器解码器使模型能够捕捉过去和未来的上下文信息。此外，我们使用汉字作为建模单元来提高模型的识别准确性。最后，在推断阶段，我们使用了递归神经网络语言模型（RNNLM）进行浅层融合。实验表明，我们的系统在评估集上的字符错误率（CER）为 38.09%，相对于官方基准线减少了 21.63%，并在挑战赛中获得了第二名。