title: Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders.
abstract: 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.
authors.affiliation: 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
authors.forename: Everthon Silva
authors.initials: ES
authors.lastname: Fonseca
authors.forename: Rodrigo Capobianco
authors.initials: RC
authors.lastname: Guido
authors.forename: Paulo Rogério
authors.initials: PR
authors.lastname: Scalassara
authors.forename: Carlos Dias
authors.initials: CD
authors.lastname: Maciel
authors.forename: José Carlos
authors.initials: JC
authors.lastname: Pereira
mesh_terms: 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*