Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients

IEEE Transactions on Biomedical Engineering

Volumn 58, Issue 2, 2011, Pages 370-379

  Arias Londoño, Julián D ^a,b   Godino Llorente, Juan I ^a   Sáenz Lechón, Nicolás ^a   Osma Ruiz, Víctor ^a   Castellanos Domínguez, Germán ^b  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

Author keywords

Combining classifiers; Gaussian mixture models (GMMs); nonlinear analysis; pathological voices; support vector machines (SVMs)

Indexed keywords

AMOUNT OF INFORMATION; AUTOMATIC DETECTION; CEPSTRAL COEFFICIENTS; COMBINING CLASSIFIERS; COMPLEXITY MEASURES; CORRELATION DIMENSIONS; DISCRIMINATIVE APPROACH; GAUSSIAN MIXTURE MODELS (GMMS); LARGEST LYAPUNOV EXPONENT; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; NEW APPROACHES; NOISE PARAMETERS; NONLINEAR STATISTICS; PATHOLOGICAL VOICE; SCALING ANALYSIS;

CLASSIFIERS; FRACTAL DIMENSION; LYAPUNOV METHODS; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES; TIME SERIES; TIME SERIES ANALYSIS;

NONLINEAR ANALYSIS;

ARTICLE; CORRELATION DIMENSION; ENTROPY; HIDDEN MARKOV MODEL; HUMAN; LARGEST LYAPUNOV EXPONENT; MEL FREQUENCY CEPSTRAL COEFFICIENT; NOISE; NOISE MEASUREMENT; SOUND INTENSITY; SOUND RELATED PHENOMENA; SPEECH DISCRIMINATION; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; VOICE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; MARKOV CHAINS; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOUND SPECTROGRAPHY; VOICE DISORDERS;

EID: 79551543561     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2010.2089052     Document Type: Article

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