hh.sePublications
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
Assessing Parkinson's disease severity using speech analysis in non-native speakers
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).ORCID iD: 0000-0002-2513-3040
Donald Gordon Brain and Mind Centre, Johannesburg, South Africa & School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.
Aquatic Rehabilitation Center, University of Johannesburg, Johannesburg, South Africa.
Show others and affiliations
2019 (English)In: Computer speech & language (Print), ISSN 0885-2308, E-ISSN 1095-8363, Vol. 61, article id 101047Article in journal (Refereed) Published
Abstract [en]

Background: Speech disorder is a common manifestation of Parkinson's disease with two main symptoms, dysprosody and dysphonia. Previous research studying objective measures of speech symptoms involved patients and examiners who were native language speakers. Measures such as cepstral separation difference (CSD) features to quantify dysphonia and dysprosody accurately distinguish the severity of speech impairment. Importantly CSD, together with other speech features, including Mel-frequency coefficients, fundamental-frequency variation, and spectral dynamics, characterize speech intelligibility in PD. However, non-native language speakers transfer phonological rules of their mother language that tamper speech assessment.

Objectives: This paper explores CSD's capability: first, to quantify dysprosody and dysphonia of non-native language speakers, Parkinson patients and controls, and secondly, to characterize the severity of speech impairment when Parkinson's dysprosody accompanies non-native linguistic dysprosody.

Methods: CSD features were extracted from 168 speech samples recorded from 19 healthy controls, 15 rehabilitated and 23 not-rehabilitated Parkinson patients in three different clinical speech tests based on Unified Parkinson's disease rating scale motor-speech examination. Statistical analyses were performed to compare groups using analysis of variance, intraclass correlation, and Guttman correlation coefficient µ2. Random forests were trained to classify the severity of speech impairment using CSD and the other speech features. Feature importance in classification was determined using permutation importance score.

Results: Results showed that the CSD feature describing dysphonia was uninfluenced by non-native accents, strongly correlated with the clinical examination (µ2>0.5), and significantly discriminated between the healthy, rehabilitated, and not-rehabilitated patient groups based on the severity of speech symptoms. However, the feature describing dysprosody did not correlate with the clinical examination but significantly distinguished the groups. The classification model based on random forests and selected features characterized the severity of speech impairment of non-native language speakers with high accuracy. Importantly, the permutation importance score of the CSD feature representing dysphonia was the highest compared to other features. Results showed a strong negative correlation (µ2<-0.5) between L-dopa administration and the CSD features.

Conclusions: Although non-native accents reduce speech intelligibility, the CSD features can accurately characterize speech impairment, which is not always possible in the clinical examination. Findings support using CSD for monitoring Parkinson's disease.

© 2019 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
London, UK: Academic Press, 2019. Vol. 61, article id 101047
Keywords [en]
Dysphonia, Dysprosody, Parkinson's disease, Speech processing, Tele-monitoring
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:hh:diva-41003DOI: 10.1016/j.csl.2019.101047Scopus ID: 2-s2.0-85075748795OAI: oai:DiVA.org:hh-41003DiVA, id: diva2:1372021
Note

Funding: Promobilia Foundation, Sweden

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2020-02-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Khan, TahaLundgren, LinaDougherty, MarkVerikas, AntanasPavel, MishaJimison, HollyNowaczyk, Sławomir

Search in DiVA

By author/editor
Khan, TahaLundgren, LinaDougherty, MarkVerikas, AntanasPavel, MishaJimison, HollyNowaczyk, SławomirAharonson, Vered
By organisation
CAISR - Center for Applied Intelligent Systems ResearchThe Rydberg Laboratory for Applied Sciences (RLAS)Halmstad Embedded and Intelligent Systems Research (EIS)
In the same journal
Computer speech & language (Print)
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 56 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