TY  - JOUR
KW  - algorithm; Article; controlled study; diagnostic accuracy; fractal analysis; glottis; human; phonation; signal detection; speech analysis; vibration; vocal cord; voice disorder; wavelet analysis; pathophysiology; physiology; voice; Voice Disorders; wavelet analysis
KW  -  Algorithms; Fractals; Humans; Voice; Voice Disorders; Wavelet Analysis
EP  - 10
AV  - none
N2  - Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1â??1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28  is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45  and an area under receiver operating characteristic curve (AUC) of 95.06  is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26  in accuracy and 1.45  in AUC is observed. © 2015, Springer Science+Business Media New York.
VL  - 40
JF  - Journal of Medical Systems
N1  - cited By 50
SP  - 1
IS  - 1
TI  - Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals
SN  - 01485598
ID  - scholars8019
A1  - Ali, Z.
A1  - Elamvazuthi, I.
A1  - Alsulaiman, M.
A1  - Muhammad, G.
Y1  - 2016///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946047730&doi=10.1007%2fs10916-015-0392-2&partnerID=40&md5=e3a6a253d5fa106d6c5e7ee9f4ceca60
PB  - Springer New York LLC
ER  -