English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorderthat causes cognitive disturbances, neuropsychiatric symptoms, and impairedmotor abilities (e.g., gait, speech, voice). Due to its progressive nature, HDtreatment requires ongoing clinical monitoring of symptoms. Individuals withthe gene mutation which causes HD may exhibit a range of speech symptoms asthey progress from premanifest to manifest HD. Differentiating betweenpremanifest and manifest HD is an important yet understudied problem, as thisdistinction marks the need for increased treatment. Speech-based passivemonitoring has the potential to augment clinical assessments by continuouslytracking manifestation symptoms. In this work we present the firstdemonstration of how changes in connected speech can be measured todifferentiate between premanifest and manifest HD. To do so, we focus on a keyspeech symptom of HD: vowel distortion. We introduce a set of vowel featureswhich we extract from connected speech. We show that our vowel features candifferentiate between premanifest and manifest HD with 87% accuracy.

Classification of Manifest Huntington Disease using Vowel Distortion Measures