Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
  • asciidoc
  • rtf
Cepstral separation difference: a novel approach for speech impairment quantification in Parkinson’s disease
Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University. (PAULINA)ORCID iD: 0000-0002-2752-3712
Dalarna University, School of Technology and Business Studies, Computer Engineering.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, p. 25-34Article in journal (Refereed) Published
Abstract [en]

This paper introduces a novel approach, Cepstral Separation Difference (CSD), for quantification of speech impairment in Parkinson’s disease (PD). CSD represents a ratio between the magnitudes of glottal (source) and supra-glottal (filter) log-spectrums acquired using the source-filter speech model. The CSD-based features were tested on a database consisting of 240 clinically rated running speech samples acquired from 60 PD patients and 20 healthy controls. The Guttmann (µ2) monotonic correlations between the CSD features and the speech symptom severity ratings were strong (up to 0.78). This correlation increased with the increasing textual difficulty in different speech tests. CSD was compared with some non-CSD speech features (harmonic ratio, harmonic-to-noise ratio and Mel-frequency cepstral coefficients) for speech symptom characterization in terms of consistency and reproducibility. The high intra-class correlation coefficient (>0.9) and analysis of variance indicates that CSD features can be used reliably to distinguish between severity levels of speech impairment. Results motivate the use of CSD in monitoring speech symptoms in PD.

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 34, no 1, p. 25-34
Keywords [en]
Parkinson's disease; Speech processing; Dysarthria; Acoustic analysis; Speech cepstrum
National Category
Engineering and Technology
Research subject
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
Identifiers
URN: urn:nbn:se:du-12730DOI: 10.1016/j.bbe.2013.06.001ISI: 000333226500005OAI: oai:DiVA.org:du-12730DiVA, id: diva2:638297
Funder
Knowledge FoundationAvailable from: 2013-07-29 Created: 2013-07-29 Last updated: 2015-07-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Khan, TahaWestin, JerkerDougherty, Mark
By organisation
Computer Engineering
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 717 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
  • asciidoc
  • rtf