Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders.
This work describes a novel algorithm to identify laryngeal pathologies, by the digital analysis of the voice. It is based on Daubechies' discrete wavelet transform (DWT-db), linear prediction coefficients (LPC), and least squares support vector machines (LS-SVM). Wavelets with different support-sizes and three LS-SVM kernels are compared. Particularly, the proposed approach, implemented with modest computer requirements, leads to an adequate larynx pathology classifier to identify nodules in vocal folds. It presents over 90% of classification accuracy and has a low order of computational complexity in relation to the speech signal's length.
Fonseca
Everthon Silva
ES
SEL/EESC/USP and IFSC/USP - Department of Electrical Engineering, School of Engineering at São Carlos and Institute of Physics at Sao Carlos, University of São Paulo, SP, Brazil. everthon@sel.eesc.usp.br
Guido
Rodrigo Capobianco
RC
Scalassara
Paulo Rogério
PR
Maciel
Carlos Dias
CD
Pereira
José Carlos
JC
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003196:Computer Graphics; D005260:Female; D006801:Humans; D007818:Laryngeal Diseases / Q000175:diagnosis; D016018:Least-Squares Analysis; D016014:Linear Models; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D010700:Phonetics; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013068:Speech Production Measurement; D014832:Voice Disorders / Q000175:diagnosis*