Feature extracted from wavelet eigenfunction estimation for text-independent speaker recognition
A new speaker feature extracted from wavelet eigenfunction estimation is described. The signal is decomposed through interpolating the 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 KarhunenLoeve transform (KLT) with respect to the percentages of recognition.
speaker recognition karhunenloeve transform wavelet transform