Artificial Intelligence-Based Framework For Automatic Detection Of Dysarthria Severity Levels Using Speech Analysis

Speech disorders significantly affect an individual's ability to communicate effectively and reduce overall quality of life. Dysarthria is a neurological speech disorder that results from damage to the nervous system and affects the muscles involved in speech production. Traditional assessment of dysarthria severity is usually performed by speech-language pathologists through perceptual evaluation, which can be subjective and time-consuming. Recent advancements in artificial intelligence and machine learning have enabled the development of automated systems capable of analysing speech characteristics and identifying different levels of dysarthria severity. This study presents an overview of intelligent techniques used for the automatic detection and classification of dysarthria severity levels. The proposed approach focuses on analysing speech features such as acoustic patterns, prosodic characteristics, and spectral features extracted from speech signals. Machine learning and deep learning models are then used to classify the severity of dysarthria based on these extracted features. By utilizing AI-based models, the system can provide objective and efficient evaluation of speech impairments. The proposed framework can assist clinicians in improving diagnostic accuracy and developing personalized rehabilitation strategies for individuals affected by dysarthria.