A robust wavelet-based text-independent speaker identification

This study proposes novel robust text-independent speaker identification based on the Discrete Wavelet Transform (DWT), the Mel-Frequency Discrete Wavelet Coefficients (MFD WC), the wavelet-based sub-band weighting and the Likelihood Combination Gaussian Mixture Model (LCGMM). This method is used in the text-independent speaker identification in compare to the widely used MFCC features recognizer, full-band recognizer and equal sub-band weighting recognizer. Our experimental results show that our proposal achieves higher recognition rate than the others for our Vietnamese speech corpus with clean and white noisy speech.