Automated tongue-twister phrase-based screening for Cerebellar Ataxia using Vocal tract Biomarkers∗

Kashyap, Bipasha, Pathirana, Pubudu, Horne, M, Power, L and Szmulewicz, D 2019, Automated tongue-twister phrase-based screening for Cerebellar Ataxia using Vocal tract Biomarkers∗, in EMBC 2019 : Proceedings of the IEEE Engineering in Medicine & Biology Society 2019 Annual International Conference, IEEE, Piscataway, N.J., pp. 7173-7176, doi: 10.1109/EMBC.2019.8857868.

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Title Automated tongue-twister phrase-based screening for Cerebellar Ataxia using Vocal tract Biomarkers∗
Author(s) Kashyap, BipashaORCID iD for Kashyap, Bipasha orcid.org/0000-0002-9469-858X
Pathirana, PubuduORCID iD for Pathirana, Pubudu orcid.org/0000-0001-8014-7798
Horne, M
Power, L
Szmulewicz, D
Conference name IEEE Engineering in Medicine & Biology Society. International Conference. (41st 2019 : Berlin, Germany)
Conference location Berlin, Germany
Conference dates 2019/07/23 - 2019/07/27
Title of proceedings EMBC 2019 : Proceedings of the IEEE Engineering in Medicine & Biology Society 2019 Annual International Conference
Publication date 2019
Start page 7173
End page 7176
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) speech biomarker
linear predictive coding
spectrogram
vocal tract measures
acoustic analysis
cerebellar ataxia
Summary © 2019 IEEE. Cerebellar Ataxia (CA) is a neurological condition that leads to uncoordinated muscle movements, even affecting the production of speech. Effective biomarkers are necessary to produce an objective decision-making support tool for early diagnosis of CA in non-clinical environments. This paper investigates the reliability and effectiveness of vocal tract acoustic biomarkers for assessing CA speech. These features were tested on a database consisting of 52 clinically rated tongue-twister phrase 'British Constitution' and its 4 consonant-vowel (CV) excerpts /ti/, /ti/', /tu/, /tion/ acquired from 30 ataxic patients and 22 healthy controls. Such a marker could be applied to objectively assess the severity of CA from a simple speaking test, contributing to the possibility of being translated into a computer based automatic module to screen the disease from the speech. All the vocal tract features explored in this study were statistically significant using Kolmogorov-Smirnov test at 5% level in distinguishing healthy and CA speech. Several machine learning classifiers with 5-fold cross-validations were implemented on the vocal features. It was observed that the intensity ratios corresponding to the 4 C-V excerpts in CA group showed an increased variability and produced the best classification accuracy of 84.6% using KNN classifier. Results motivate the use of vocal tract features for monitoring CA speech.
ISBN 9781538613115
ISSN 1557-170X
DOI 10.1109/EMBC.2019.8857868
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30134064

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