Harnessing Deep Learning Techniques for Dysarthria Detection

Dysarthria, a motor speech disorder resulting from neurological impairments. This study explores various approaches for the automated detection of dysarthria, integrating both traditional and emerging technologies. Speech assessments by acoustic analysis, machine learning models, and deep learning techniques are considered. Machine learning models are trained on datasets containing normal and dysarthric speech for males and females, while deep learning methods, including convolutional and recurrent neural networks, are employed to automatically learn features from speech data. Our goal is to develop a reliable and accessible dysarthria detection system thatcomplements the expertise of healthcare professionals. Validation using diverse datasets is emphasized, acknowledging the importance of early detection and intervention in improving outcomes for individuals with dysarthria.