Augmentative Alternative Communication and Text-to-Speech for Dyslexia, Autism, and Parkinson’s using Machine Learning

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Manupati Jaideep

Augmentative Alternative Communication and Text-to-Speech for Dyslexia, Autism, and Parkinson’s using Machine Learning

Authors: Manupati Jaideep

Unique Paper ID: 169267
Volume: 11
Issue: 6
PageNo: 1180-1186

Keywords: No Keywords Found

Abstract:
In the context of addressing communication challenges faced by individuals with diverse neurodevelopmental and neurological disorders, this project focuses on the development of an advanced speech synthesis system. Leveraging state- of-the-art technologies such as Convolutional Neural Networks (CNN) and Long Short-Term Memory Recurrent Neural Net- works (LSTM-RNN), the system is designed to produce natural and intelligible synthetic speech. This initiative is especially crucial for individuals dealing with visual impairment, Dyslexia, Parkinson’s disease, Autism Spectrum Conditions (ASC), and Amyotrophic Lateral Sclerosis (ALS). The proposed system aims to enhance communication accessibility and effectiveness for those with specific cognitive and motor challenges, contributing to a more inclusive and supportive digital environment.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 1180-1186

Augmentative Alternative Communication and Text-to-Speech for Dyslexia, Autism, and Parkinson’s using Machine Learning

Available:https://ijirt.org/article?manuscript=169267

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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