@Article{cmc.2021.016509,
AUTHOR = {Amany M. Sarhan, Nada M. Elshennawy, Dina M. Ibrahim},
TITLE = {HLR-Net: A Hybrid Lip-Reading Model Based on Deep Convolutional Neural Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {2},
PAGES = {1531--1549},
URL = {http://www.techscience.com/cmc/v68n2/42177},
ISSN = {1546-2226},
ABSTRACT = {<p>Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the speaking. This is a task of decoding the text from the speaker’s mouth movement. This paper proposes a lip-reading model that helps deaf people and persons with hearing problems 
to understand a speaker by capturing a video of the speaker and inputting 
it into the proposed model to obtain the corresponding subtitles. Using deep learning technologies makes it easier for users to extract a large number of different features, which can then be converted to probabilities of letters to obtain accurate results. Recently proposed methods for lip reading are based on sequence-to-sequence architectures that are designed 
for natural machine translation and audio speech recognition. However, in 
this paper, a deep convolutional neural network model called the hybrid lip-reading (HLR-Net) model is developed for lip reading from a video. The 
proposed model includes three stages, namely, pre-processing, encoder, and decoder stages, which produce the output subtitle. The inception, gradient, and bidirectional GRU layers are used to build the encoder, and the attention, fully-connected, activation function layers are used to build the decoder, which performs the connectionist temporal classification (CTC). In comparison with the three recent models, namely, the LipNet model, 
the lip-reading model with cascaded attention (LCANet), and attention-CTC 
(A-ACA) model, on the GRID <i>corpus</i> dataset, the proposed HLR-Net model can achieve significant improvements, achieving the CER of 4.9%, WER of 9.7%, and Bleu score of 92% in the case of unseen speakers, and the CER of 1.4%, WER of 3.3%, and Bleu score of 99% in the case of overlapped speakers.</p>
},
DOI = {10.32604/cmc.2021.016509}
}