A Novel Machine Lip Reading Model
Hongyang Huang, Song Chai, Jin Ting, Taoling Tian, Hong Chen, Di Zhang, Danni Gao
Abstract
Lip Reading is the technology of obtaining the language content by analyzing the change of the speaker’s lip shape and recognizing the information of the lip movement. Lip reading helps people with hearing disabilities understand what other people are saying, which is difficult for humans. This paper proposes a novel Lip Reading model using Transformer network, to achieve a lip reading model with high accuracy. The main process of the model includes the processing of data sets, the extraction of lip features using the pre-trained neural network, and then input into Transformer network for training. Finally, our model achieves a word-level lip reading accuracy of 45.81% on the open source GRID corpus.