Advancing Speech Disorder Diagnostics: A Comprehensive Study on Dysarthria Classification with CNN

This study article focuses on dysarthria, a speech problem that is often seen in patients with cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS). Early identification of dysarthria is crucial for successful treatments and better patient outcomes, since it has a substantial influence on communication ability. Using the TORGO database, which consists of 2000 samples of persons with and without dysarthria, spanning different genders, the research use a Convolutional Neural Network (CNN) to classify speech. The dataset includes both dysarthric and non-dysarthric individuals of both genders, with 500 samples each, captured during separate sessions. The CNN-based technique demonstrates an impressive 96% accuracy in differentiating between dysarthric and non-dysarthric instances, highlighting the promise of deep learning technology in assisting with the early detection of dysarthria and enabling prompt therapies for afflicted people. This study makes significant contributions to the advancement of diagnostic capacities and presents a potential opportunity to improve the quality of life for those with speech difficulties.