2020
INTERSPEECH
INTERSPEECH 2020
Vocal Markers from Sustained Phonation in Huntington’s Disease
Abstract
Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntington’s Disease represents a unique opportunity to design automatic sub-clinical markers, even in premanifest gene carriers. We investigated phonatory impairments as potential clinical markers and propose them for both diagnosis and gene carriers follow-up. We used two sets of features: Phonatory features and Modulation Power Spectrum Features. We found that phonation is not sufficient for the identification of sub-clinical disorders of premanifest gene carriers. According to our regression results, Phonatory features are suitable for the predictions of clinical performance in Huntington’s Disease.
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Interdisciplinary Bridge
— Computer Vision and Machine Learning
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Keyword Pioneer
— clinical marker
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio