Detecting Wilson's disease from unstructured connected speech: An embedding-based approach augmented by attention and bi-directional dependency

Abstract

Wilson's disease (WD) is a neurodegenerative genetic disorder in which dysarthria is the initial neurological symptom. Automated WD diagnosis from speech is thus a promising and clinically valuable approach. The present study investigates the feasibility of WD detection from unstructured connected speech (UCS) using the embedding-based approach augmented by the attention mechanism and bi-directional dependency. The classification experiment was conducted with a sample of 55 WD patients and 55 matched healthy individuals. We compare the proposed embedding approach with two models: the baseline method using the structured task and the model using conventional acoustic features. Results show that the embedding-based model achieves the best accuracy of 90.3 %, which is 4.2 % and 7 % better than the baseline and acoustic approaches, respectively. The bi-directional semantic dependency and attention mechanism can significantly improve detection performance. Moreover, we reveal that the duration of the UCS task affects the model performance, with favorable results achieved using approximately 30 s epochs. Our method provides new insights into the detection of dysarthria-related disorders.