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 (MFDWC), 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.