| Title: |
Toward a Speech-Based Model of Premanifest Huntington’s Disease Progression Using Deep Neural Networks |
| Authors: |
Sierra, Luis A.; Kaur, Japleen; Kwon, Namhee; Subramanian, Vinod; Brueckner, Raymond; Blaylock, Nate; O'Connell, Henry; Frank, Samuel A.; Corey-Bloom, Jody; Laganiere, Simon |
| Source: |
Digital Biomarkers ; volume 10, issue 1, page 53-62 ; ISSN 2504-110X |
| Publisher Information: |
S. Karger AG |
| Publication Year: |
2026 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1159/000549327 |
| Availability: |
https://doi.org/10.1159/000549327; https://karger.com/article-pdf/doi/10.1159/000549327 |
| Rights: |
https://creativecommons.org/licenses/by-nc/4.0/ ; https://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.175E48EC |
| Database: |
BASE |