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Audio-Visual Automatic Speech Recognition Towards Education for Disabilities.

Saswati Debnath¹, Pinki Roy², Suyel Namasudra^3,4

¹Department of Computer Science and Engineering, Alliance University, Bangalore, Karnataka, India.

Journal of Autism and Developmental Disorders

|July 12, 2022

Summary

This summary is machine-generated.

This study introduces an Audio-Visual Automatic Speech Recognition (AV-ASR) system to aid physically challenged individuals in education. The novel approach enhances visual speech recognition accuracy for improved hands-free computing and learning accessibility.

Keywords:

AV-ASR Clustering algorithm GLCM LBP-TOP MFCC Supervised learning

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Physically challenged individuals face barriers in accessing education.
Audio-Visual Automatic Speech Recognition (AV-ASR) offers hands-free computing solutions.
Visual speech recognition presents challenges in accurate lip tracking.

Purpose of the Study:

To enhance educational accessibility for physically challenged individuals.
To improve the accuracy of visual speech recognition in AV-ASR systems.
To address the limitations of current visual modality tracking.

Main Methods:

Proposed an appearance-based visual feature extraction method.
Utilized co-occurrence statistical measures for visual speech recognition.
Implemented Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) for visual speech information.

Main Results:

Achieved 76.60% accuracy for visual speech recognition.
Attained 96.00% accuracy for audio speech recognition.
Demonstrated the effectiveness of the proposed LBP-TOP and GLCM features.

Conclusions:

The proposed AV-ASR system significantly improves visual speech recognition.
This technology can enhance educational opportunities for physically challenged students.
The integration of advanced visual and audio features leads to robust speech recognition.