Classification of Huntington Disease using Acoustic and Lexical Features

TL;DR

This paper explores automatic speech analysis to differentiate Huntington Disease patients from healthy controls, aiming for a cost-effective, continuous, and unobtrusive biomarker for disease progression tracking.

Contribution

It introduces a preliminary system that uses acoustic and lexical speech features to distinguish HD from healthy speech, advancing towards practical, real-world monitoring tools.

Findings

Successfully differentiated HD from controls using speech features

Demonstrated potential for objective, continuous disease monitoring

Supports clinical diagnosis with automated speech analysis

Abstract

Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses. Speech analyses are currently conducted using either transcriptions created manually by trained professionals or using global rating scales. Manual transcription is both expensive and time-consuming and global rating scales may lack sufficient sensitivity and fidelity. Ultimately, what is needed is an unobtrusive measure that can cheaply and continuously track disease progression. We present first steps towards the development of such a system, demonstrating the ability to automatically differentiate between healthy controls and individuals with HD using speech cues. The results provide evidence that objective analyses can be used to support clinical diagnoses, moving towards the tracking of symptomatology outside of laboratory and clinical environments.