Feature extracted from wavelet eigenfunction estimation for text-independent speaker recognition

摘要

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 Karhunen–Loeve transform (KLT) with respect to the percentages of recognition.