Time -frequency analysis of vocal source signal for speaker recognition
Nengheng Zheng, P.C. Ching, Tan Lee · 2004
This paper investigates the importance of spectro-temporal characteristics of the source excitation signal for speaker recognition. We propose an effective feature extraction technique for obtaining essential time-frequency information from the linear prediction (LP) residual signal, which are closely related to the glottal excitation of individual speaker. With pitch synchro-nous analysis, wavelet transform is applied to every two pitch cycles of the LP residual signal to generate a new feature vector, called Wavelet Octave Coefficients of Residues (WOCOR), which provides additional speaker discriminative power to the commonly used linear predictive Cepstral coefficients (LPCC). Experimental evaluation over a Cantonese speaker recognition corpus demonstrates the effectiveness of WOCOR for speaker recognition. Recognition tests with WOCOR and LPCC outperforms the conventional methods of using Mel Frequency Cepstral Coefficients (MFCC). 1.