Voiced/unvoiced transitions in speech as a potential bio-marker to detect Parkinson's disease

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00239926" target="_blank" >RIV/68407700:21230/15:00239926 - isvavai.cz</a>
Result on the web
<a href="https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2015/Orozco-Arroyave15-VTI.pdf" target="_blank" >https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2015/Orozco-Arroyave15-VTI.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Voiced/unvoiced transitions in speech as a potential bio-marker to detect Parkinson's disease
Original language description
Several studies have addressed the automatic classification of speakers with Parkinson's disease (PD) and healthy controls (HC). Most of the studies are based on speech recordings of sustained vowels, isolated words, and single sentences. Only few investigations have considered read texts and/or spontaneous speech. This paper addresses two main questions still open regarding the automatic analysis speech in patients with PD, (a) "Is it possible to classify PD patients and HC through running speech signals in multiple languages", and (b) "where is the information to discriminate between speech recordings of PD patients and HC" In this paper speech recordings of read texts and monologues spoken in three different languages are considered. The energy content of the borders between voiced and unvoiced sounds is modeled. According to the results with read texts it is possible to achieve accuracies ranging from 91% to 98% depending on the language. With respect to the results on monologues,
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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