Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders

Authors:
Everthon Silva Fonseca;Rodrigo Capobianco Guido;Paulo Rogério Scalassara;Carlos Dias Maciel;José Carlos Pereira
Affiliations:
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 and EE/UCL ...;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;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;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;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
Venue:
Computers in Biology and Medicine
Year:
2007

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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.