Comput. Biol. Med. - Wavelet adaptation for automatic voice disorders sorting.

Tópicos

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Resumo

Early diagnosis of voice disorders and abnormalities by means of digital speech processing is a subject of interest for many researchers. Various methods are introduced in the literature, some of which are able to extensively discriminate pathological voices from normal ones. Voice disorders sorting, on the other hand, has received less attention due to the complexity of the problem. Although, previous publications show satisfactory results in classifying one type of disordered voice from normal cases, or two different types of abnormalities from each other, no comprehensive approach for automatic sorting of vocal abnormalities has been offered yet. In this paper, a solution for this problem is suggested. We create a powerful wavelet feature extraction approach, in which, instead of standard wavelets, adaptive wavelets are generated and applied to the voice signals. Orthogonal wavelets are parameterized via lattice structure and then, the optimal parameters are investigated through an iterative process, using the genetic algorithm (GA). GA is guided by the classifier results. Based on the generated wavelet, a wavelet-filterbank is constructed and the voice signals are decomposed to compute eight energy-based features. A support vector machine (SVM) then classifies the signals using the extracted features. Experimental results show that six various types of vocal disorders: paralysis, nodules, polyps, edema, spasmodic dysphonia and keratosis are fully sorted via the proposed method. This could be a successful step toward sorting a larger number of abnormalities associated with the vocal system.

Resumo Limpo

earli diagnosi voic disord abnorm mean digit speech process subject interest mani research various method introduc literatur abl extens discrimin patholog voic normal one voic disord sort hand receiv less attent due complex problem although previous public show satisfactori result classifi one type disord voic normal case two differ type abnorm comprehens approach automat sort vocal abnorm offer yet paper solut problem suggest creat power wavelet featur extract approach instead standard wavelet adapt wavelet generat appli voic signal orthogon wavelet parameter via lattic structur optim paramet investig iter process use genet algorithm ga ga guid classifi result base generat wavelet waveletfilterbank construct voic signal decompos comput eight energybas featur support vector machin svm classifi signal use extract featur experiment result show six various type vocal disord paralysi nodul polyp edema spasmod dysphonia keratosi fulli sort via propos method success step toward sort larger number abnorm associ vocal system

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