Visual Speech recognition using lip movement for deaf people using deep learning
Deep Learning, Image processing, clasisifcation, deep learning, feature extraction, feature selection
There has been a growing interest in creating automatic lip-reading systems (ALR). Methods based on Deep Learning (DL), like other computer vision applications, have grown in popularity and allowed for significant improvements in performance. The audio-visual speech recognition approach attempts to boost noise-robustness in mobile situations by extracting lip movement from side-face pictures. Although most earlier bimodal speech recognition algorithms used frontal face (lip) pictures, these approaches are difficult for consumers to utilise because they need them to speak while holding a device with a camera in front of their face. Our suggested solution, which uses a tiny camera put in a phone to capture lip movement, is more natural, simple, and convenient. This approach also successfully prevents a reduction in the input speech's signal-to-noise ratio (SNR). Optical-flow analysis extracts visual characteristics, which are then coupled with auditory data in the context of DCNN-based recognition. We employ DCNN for audio-visual speech recognition in this paper; specifically, we leverage deep learning from audio and visual characteristics for noise-resistant speech recognition. In the experimental analysis we achieved around 90% accuracy on real time test data that provides higher accuracy than traditional deep learning algorithm.
Article Details
Unique Paper ID: 155001

Publication Volume & Issue: Volume 8, Issue 12

Page(s): 998 - 1002
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