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Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words
Author(s) -
OrozcoArroyave J. R.,
Hönig Florian,
AriasLondoño J. D.,
VargasBonilla J. F.,
Nöth Elmar
Publication year - 2015
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12106
Subject(s) - cepstrum , computer science , speech recognition , mel frequency cepstrum , discriminative model , prosody , artificial intelligence , fluency , phonation , correctness , pattern recognition (psychology) , feature extraction , audiology , mathematics , medicine , algorithm , mathematics education
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.