Toward a Speech-Based Model of Premanifest Huntington's Disease Progression Using Deep Neural Networks.

INTRODUCTION

Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by motor, cognitive, and psychiatric decline. The Unified Huntington's Disease Rating Scale Total Motor Score (UHDRS-TMS) is standard for staging manifest disease, but is relatively insensitive to subtle premanifest changes. Speech abnormalities are emerging as candidate digital biomarkers; however, reliably separating premanifest HD (preHD) from healthy controls remains challenging. Here, we assess the feasibility of a speech-only approach by training and comparing multiple classifiers across diverse feature sets and structured tasks to determine whether speech alone can discriminate preHD from controls.

METHODS

Speech samples were collected from 94 individuals with HD (38 premanifest, 56 manifest) and 36 controls using a standardized six-task protocol administered via tablet. From these recordings, 188 lexical and prosodic features were automatically extracted. We trained 4 machine learning classifiers: random forest, support vector machine, XGBoost, and deep neural networks (DNNs), within 10-fold cross-validation using three feature configurations: (1) all tasks (188 features), (2) the top 30 ANOVA-ranked features, and (3) 22 features from the Caterpillar passage alone.

RESULTS

Traditional classifiers showed limited accuracy. A DNN using only the Caterpillar task achieved 81% unweighted accuracy for classifying preHD versus controls. Accuracy increased to 83% for prodromal HD and 87% when all HD participants were compared to controls. Adding features from additional tasks did not improve performance.

CONCLUSION

A brief, structured speech task combined with deep learning enabled accurate classification of preHD. These findings support speech analysis as a scalable, objective tool for early disease detection and monitoring.