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At least 217 records · Page 12Linked to original sources
PubMed2006

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.

Adolescent↗
PubMed1995

[Cepstrum analysis in voice disorders].

Objective data and reproducible procedures are increasingly demanded for assessment and long-term comparison in voice disturbances. Well-known electroacoustic methods like formant analysis, autocorrelation and inverse filtering have been applied for this purpose, though without leaving experimental stage. The cepstrum analysis presented here permits an easy and distinct separation of glottal pitch and filter function of the vocal tract. In this study, 112 normal and hoarse voices were analyzed. The sum of amplitudes of the first cepstral pitch peaks differed significantly between the different degrees of hoarseness (0-3). Disturbances producing hoarseness, however, could not be sufficiently differentiated by the method in its present state.

Adult↗