Feature Extracted from Wavelet Eigenfunction Estimation for Speaker Recognition

S.-Y. Lung and H.-C. Ma (Taiwan)


Speaker recognition, Wavelettransform, Karhunen-Loeve transform


A new speaker feature extracted from wavelet eigenfunction estimation is described. The signal is decomposed through interpolating scaling function. Wavelets can offer a significant computational advantage by reducing the dimensionality of the eigenvalue problem. Our results have shown that this wavelet feature introduced better performance than the other Karhunen-Loeve transform(KLT) with respect to the percentages of recognition.

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