ArticleInterspeech2020
Classification of Manifest Huntington Disease using Vowel Distortion Measures.
Article in Interspeech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 0 citations in OpenAlex.
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- Predicting clinical scores in Huntington's disease: a lightweight speech test.Journal of neurology · 2022Article
- Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease.Journal of neurology · 2022Article
- Toward a Speech-Based Model of Premanifest Huntington's Disease Progression Using Deep Neural Networks.Digital biomarkersArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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 Huntingtin gene mutation, which causes HD, may exhibit a range of speech symptoms as they progress from premanifest to manifest HD. Speech-based passive monitoring has the potential to augment clinical information by more continuously tracking manifestation symptoms. Differentiating between premanifest and manifest HD is an important yet under-studied problem, as this distinction marks the need for increased treatment. In this work we present the first demonstration of how changes in speech can be measured to differentiate between premanifest and manifest HD. To do so, we focus on one speech symptom of HD: distorted vowels. We introduce a set of Filtered Vowel Distortion Measures (FVDM) which we extract from read speech. We show that FVDM, coupled with features from existing literature, can differentiate between premanifest and manifest HD with 80% accuracy.
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Registered trials
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