Using Lip Reading Recognition to Predict Daily Mandarin Conversation

• Published in: IEEE access Vol. 10; pp. 53481 - 53489
• Main Authors: Haq, Muhamad Amirul, Ruan, Shanq-Jang, Cai, Wen-Jie, Li, Lieber Po-Hung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Audio data; Automatic speech recognition; Background noise; Conversation; Convolution; Datasets; Deep learning; Feature extraction; hearing aid; Hearing aids; Hidden Markov models; Lip reading; Lipreading; Lips; Mandarin; Mandarin lip reading; Neural networks; Oral communication; Reading; speech aid; Speech recognition; Tuition; Videos; visual speech recognition; Visual tasks; Visualization; Voice recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3175867

Audio-based automatic speech recognition as a hearing aid is susceptible to background noise and overlapping speeches. Consequently, audio-visual speech recognition has been developed to complement the audio input with additional visual information. However, the huge improvement of neural networks in the visual task has resulted in a robust and reliable lip reading framework that can recognize speech from visual input alone. In this work, we propose a lip reading recognition model to predict daily Mandarin conversation and collect a new Daily Mandarin Conversation Lip Reading (DMCLR) dataset, consisting of 1,000 videos from 100 daily conversations spoken by ten speakers. Our model consists of a spatiotemporal convolution layer, a SE-ResNet-18 network, and a back-end module consisting of bi-directional gated recurrent unit (Bi-GRU), 1D convolution, and fully-connected layers. This model is able to reach 94.2% of accuracy in the DMCLR dataset. Such performance makes it possible for Mandarin lip reading applications to be practical in real life. Additionally, we are able to achieve 86.6% and 57.2% accuracy on Lip Reading in the Wild (LRW) and LRW-1000 (Mandarin), respectively. The results show that our method achieves state-of-the-art performance on these two challenging datasets.