基于时域卷积网络的中文句子级唇语识别算法.

Existing lip-reading algorithms for Chinese sentences are inadequate at feature extraction and visual ambiguity resolution, which leads to low accuracy. Aiming at this problem, this paper proposed a lip-reading algorithm for Chinese sentences based on temporal convolutional network and 3D convolutional neural network（3DT-CHLipNet）.Firstly, it used a Temporal Convolutional Network to extract long-term features from lip dynamics sequences, which has a much larger receptive field than the Long Short Term Memory Network. Secondly, in order to minimize the visual ambiguity in Chinese lipreading, it adopted a Transformer model with the self-attention mechanism to capture the context information and improve the accuracy of sentence prediction. Finally, it introduced a temporal masking data augmentation strategy in the data preprocessing to further reduce the error rate of the algorithm. Comparison experiments on CMLR, the largest open-source Sequence-to-Sequence Chinese Mandarin Lip Reading dataset, show that the improvement in accuracy over representative lip reading algorithms for Chinese sentences ranges from 2.17%to 23.99%. [ABSTRACT FROM AUTHOR]