Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/4731
Title: Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Authors: Debnath, Saswati
Roy, Pinki
Namasudra, Suyel
Crespo, Ruben Gonzalez
Keywords: AV-ASR
LBP-TOP
GLCM
MFCC
Clustering algorithm
Supervised learning
Issue Date: 12-Jul-2022
Publisher: Journal of Autism and Developmental Disorders
Abstract: Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition.
URI: https://doi.org/10.1007/s10803-022-05654-4
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/4731
ISSN: 1573-3432
0162-3257
Appears in Collections:Journal Articles

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