This letter presents text-independent speaker identification results for telephone speech. A speaker identification system based on Karhunen-Loeve transform (KLT) derived from codebook design using genetic algorithm (GA) is proposed. We have combined genetic algorithm (GA) and the vector quantization (VQ) algorithm to avoid typical local minima for speaker data compression. Identification accuracies of 91% were achieved for 100 Mandarin speakers.

A wavelet feature selection derived by using fuzzy evaluation index for speaker identification is described. The concept of a flexible membership function incorporating weighed distance is introduced in the evaluation index to make the modeling of clusters more appropriate. Our results have shown that this feature selection introduced better performance than the wavelet features with respect to the percentages of recognition.

A speaker identification system based on wavelet transform (WT) derived from codebook design using fuzzy c-mean algorithm (FCM) is proposed. We have combined FCM and the vector quantization (VQ) algorithm to avoid typical local minima for speaker data compression. Identification accuracies of 94% were achieved for 100 Mandarin speakers.

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.