A new speaker feature extracted from multi-wavelet decomposition for speaker recognition is described. The multi-wavelet decomposition is a multi-scale representation of the covariance matrix. We have combined wavelet transform and the multi-resolution singular value algorithm to decompose eigenvector for speaker feature extraction not at the square matrix. Our results have shown that this multi-wavelet feature introduced better performance than the cepstrum and Δ-cepstrum with respect to the percentages of recognition.
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