Abstract
Novel Algorithm Utilizing Deep Learning for Enhanced Arabic Lip Reading Recognition
Highlights
Computerized lip reading is the viseme understanding of lips movements and convert it to written text for both the hearing impaired and for other speakers in noisy environment [1]
We should understand that the results accuracies are attained after the pre-training process through fine tuning of the deep CNN, where it is unrolled to its Deep learning (DNN) architecture, and the decoder is used to convert the outputs to the appropriate classes
This research proposed an approach with two phases of viseme feature extraction and deep CNN classification
Summary
Computerized lip reading is the viseme understanding of lips movements and convert it to written text for both the hearing impaired and for other speakers in noisy environment [1]. Real-time viseme recognition systems are very crucial in different paradigms such as surveillance, as a hearing aid utilized in video conferencing for video games with lip reading interfaces especially in noisy environments. Most of these applications anticipate a specific response time, usually real time. Real-time viseme recognition systems is driven by the lip reading movement recognition using video processing techniques. Skin color segmentation can differentiate between lip area and other areas in the video frames We can apply it to determine lip-like pixels in an image which can be a binary classification problem.
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