Classification of Manifest Huntington Disease using Vowel Distortion Measures

TL;DR

This paper introduces a novel speech-based method to differentiate premanifest and manifest Huntington disease by analyzing vowel distortion in connected speech, achieving 87% accuracy, thus aiding clinical monitoring.

Contribution

It presents the first approach to measure vowel distortion features from connected speech to distinguish between premanifest and manifest HD.

Findings

Vowel features can differentiate HD stages with 87% accuracy.

Connected speech analysis provides a non-invasive monitoring tool.

Vowel distortion is a key speech symptom in HD progression.

Abstract

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorder that causes cognitive disturbances, neuropsychiatric symptoms, and impaired motor abilities (e.g., gait, speech, voice). Due to its progressive nature, HD treatment requires ongoing clinical monitoring of symptoms. Individuals with the gene mutation which causes HD may exhibit a range of speech symptoms as they progress from premanifest to manifest HD. Differentiating between premanifest and manifest HD is an important yet understudied problem, as this distinction marks the need for increased treatment. Speech-based passive monitoring has the potential to augment clinical assessments by continuously tracking manifestation symptoms. In this work we present the first demonstration of how changes in connected speech can be measured to differentiate between premanifest and manifest HD. To do so, we focus on a key speech symptom of HD: vowel distortion. We introduce a set of vowel features which we extract from connected speech. We show that our vowel features can differentiate between premanifest and manifest HD with 87% accuracy.