Dysarthric Speech Classification: A Comparative Analysis of Decision-Support Methods

This study compares data-driven and non-data-driven methods for classifying dysarthric speech employing the UA-Speech dataset. Data-driven deep-learning architectures, including MobileNetV3, ResNet50, and ResNet152, are evaluated alongside non-data-driven approaches such as pre-trained Automatic Speech Recognition (ASR) based methods and DSP-based methods. The findings reveal that overlapping patient data between training and testing sets leads to inflated performance metrics. By enforcing patient-separated evaluations, the study highlights reduced model accuracy, emphasizing the need for methodologies that generalize effectively to unseen data. Among the tested approaches, the ASR-based approach demonstrates the highest potential, achieving strong correlation with human intelligibility assessments and offering promising clinical applicability. This analysis underscores the importance of robust evaluation protocols and highlights the potential of pre-trained ASR models for reliable dysarthric speech assessment.