Abstract
Spearker recognition used widely in our lives is an important branch of authenticating automatically a speaker's identity based on human biological feature. Linear Prediction Cepstrum Coefficient (LPCC) and Mel Frequency Cepstrum Coefficient (MFCC) are used as the features for text-independent speaker recognition in this system. And the experiments compare the recognition rate of LPCC, MFCC or the combination of LPCC and MFCC through using Vector Quantization (VQ) and Dynamic Time Warping (DTW) to recognize a speaker's identity. It proves that the combination of LPCC and MFCC has a higher recognition rate.
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