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Classification of speech intelligibility in Parkinson's disease: Speech Impairment Classification
Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden. (PAULINA)ORCID iD: 0000-0002-2752-3712
Dalarna University, School of Technology and Business Studies, Computer Engineering. (PAULINA)ORCID iD: 0000-0003-0403-338X
Dalarna University, School of Technology and Business Studies, Computer Engineering. (PAULINA)
2014 (English)In: Biocybernetics and Biomedical Engineering, ISSN 0208-5216, Vol. 34, no 1, 35-45 p.Article in journal (Refereed) Published
Abstract [en]

A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 34, no 1, 35-45 p.
Keyword [en]
Parkinson's disease, Speech processing, Dysarthria, Support vector machine, Tele-monitoring.
National Category
Engineering and Technology
Research subject
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifiers
URN: urn:nbn:se:du-13130DOI: 10.1016/j.bbe.2013.10.003ISI: 000333226500006OAI: oai:DiVA.org:du-13130DiVA: diva2:655236
Projects
PAULINA
Funder
Knowledge Foundation
Available from: 2013-10-10 Created: 2013-10-10 Last updated: 2015-07-01Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
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