Paper Title
AUDIO VISUAL SPEECH RECOGNITION FOR HEARING IMPAIRED CHILDREN
Article Identifiers
Authors
NANDITHA N E , Srikanth S , Namitha C , Nasim Akhtar
Keywords
Abstract
Abstract: Assistive technology holds tremendous promise in aiding individuals with hearing impairments through the utilization of Audio-Visual Speech Recognition (AVSR). Approximately 466 million people globally contend with hearing loss, with hearing-impaired individuals often relying on lip reading to comprehend speech. Challenges such as a shortage of trained sign language interpreters and the prohibitive costs of assistive devices further compound the difficulties faced by these individuals. To bridge these gaps, our research endeavours to leverage advanced deep learning models to develop a novel visual speech recognition technique. Unlike existing VSR methods, which suffer from inaccuracies, our approach involves integrating outcomes from both audio and visual speech modalities. Our study introduces a cutting-edge deep learning- based audio-visual speech recognition model tailored for efficient lip reading. Through our efforts, we aim to enhance system performance, achieving a notable reduction in word error rate to approximately 6.59% for the ASR system and a lip-reading model accuracy of around 95%.
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How To Cite (APA)
NANDITHA N E, Srikanth S, Namitha C, & Nasim Akhtar (July-2024). AUDIO VISUAL SPEECH RECOGNITION FOR HEARING IMPAIRED CHILDREN. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), g232-g240. https://ijnrd.org/papers/IJNRD2407526.pdf
Issue
Volume 9 Issue 7, July-2024
Pages : g232-g240
Other Publication Details
Paper Reg. ID: IJNRD_225725
Published Paper Id: IJNRD2407526
Downloads: 000121984
Research Area: Engineering
Country: BANGALORE, KARNATAKA, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2407526.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407526
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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