An end-to-end lipreading network combined with ResNet50 and Bi-GRU

YanMei Li; XingYu Wang; WeiWu Ding; JingHong Tang; LiHong Li

doi:10.1117/12.2624702

13 December 2021 An end-to-end lipreading network combined with ResNet50 and Bi-GRU

YanMei Li, XingYu Wang, WeiWu Ding, JingHong Tang, LiHong Li

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 1208706 (2021) https://doi.org/10.1117/12.2624702
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Automatic lip-reading (ALR), also known as visual speech recognition (VSR), refers to the movement of lips to acquire the content of a speaker. In recent years, the introduction of deep learning has brought great breakthroughs to lip-reading research. Compared with traditional methods, this method can extract depth features more conveniently from large-scale data sets. We proposed an end-to-end deep learning network model containing three construction modules, STCNN, ResNet50, and Bi-GRU. STCNN was used to extract deep features and Bi-GRU was used for feature recognition. Meanwhile, we verified the effectiveness of Bi-GRU through comparative experiments. The effects of different ResNet in the whole model were compared. Finally, the accuracy of characters in the corpus reached 95.7%.

Citation Download Citation

YanMei Li, XingYu Wang, WeiWu Ding, JingHong Tang, and LiHong Li "An end-to-end lipreading network combined with ResNet50 and Bi-GRU", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 1208706 (13 December 2021); https://doi.org/10.1117/12.2624702

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KEYWORDS

Feature extraction

Performance modeling

Convolution

Laser induced plasma spectroscopy

Speech recognition

Visualization

Motion models

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