Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words

About 1% of people older than 65 years suffer from Parkinson's disease (PD) and 90% of them develop several speech impairments, affecting phonation, articulation, prosody and fluency. Computer‐aided tools for the automatic evaluation of speech can provide useful information to the medical exper...

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Bibliographic Details
Published in:Expert systems 2015-12, Vol.32 (6), p.688-697
Main Authors: Orozco-Arroyave, J. R., Hönig, Florian, Arias-Londoño, J. D., Vargas-Bonilla, J. F., Nöth, Elmar
Format: Article
Language:English
Subjects:
Analysis
Automation
Cepstral analysis
linear prediction
Medical diagnosis
Monitoring systems
Parkinson's disease
Patients
Recording
relative spectra analysis
Spanish language
Spectra
Speech
Speech disorders
speech spectrum
Studies
Vowels
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Summary:About 1% of people older than 65 years suffer from Parkinson's disease (PD) and 90% of them develop several speech impairments, affecting phonation, articulation, prosody and fluency. Computer‐aided tools for the automatic evaluation of speech can provide useful information to the medical experts to perform a more accurate and objective diagnosis and monitoring of PD patients and can help also to evaluate the correctness and progress of their therapy. Although there are several studies that consider spectral and cepstral information to perform automatic classification of speech of people with PD, so far it is not known which is the most discriminative, spectral or cepstral analysis. In this paper, the discriminant capability of six sets of spectral and cepstral coefficients is evaluated, considering speech recordings of the five Spanish vowels and a total of 24 isolated words. According to the results, linear predictive cepstral coefficients are the most robust and exhibit values of the area under the receiver operating characteristic curve above 0.85 in 6 of the 24 words.
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.12106