Study Of Speaker Recognition System

Speaker recognition as one of the biometrics techniques is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics specific to each individual. Speaker recognition has caught many attentions for its particularly advantage on convenience, economy and veracity and become an important and popular authentication technique in human life and work. Therefore, a more robust method for speaker recognition with high accuracy of recognition rate is the aim for researchers at home and abroad.By analyzing the general principles and system structure of speaker recognition and considerating subsistent technology of speaker recognition, Linear prediction cepstrum coefficient(LPCC) and Mel cepstrum coefficient(MFCC) are adopted as characteristic parameters, the vector quantization(VQ)is used as speaker recognition method to set up speaker recognition system. To improve the recognition effect, the tasks are made as follows:Firstly, endpoint detection is studied, some classic endpoint detection methods are discussed here, such as: short-time energy, average zero-crossing rate, based on fractal dimension after wavelet transform, based on spectrum variance. The related results all show the characteristics of their own. By analyzing the faults of those algorithms, endpoint detection algorithms based on adaptive subband spectral entropy and power entropy are proposed, the experimental results prove their superiority.Secondly, feature extraction is studied, It mainly studied some common characteristic parameters of speech such as LPC, LPCC and MFCC. MFCC and LPCC are theoretically stated. And a new feature, that is perceptual cepstral coefficients based on the minimum variance distortless response(PMCC), is proposedThirdly, speaker recognition is studied, some methods of speaker recognition are presented, such as DTW, VQ, HMM, GMM, ANN and SVM. Espeacilly, the basic principle and application of VQ are detailedly presented. Meanwhile an improved VQ is proposed and it is as the method of this recognition system.Finally, the realization progress of this system is studied. LPCC and MFCC are extracted. The speaker recognition experiments are made using LPCC and MFCC based on VQ in different capacity and time., and then based on improved VQ in different time. Experiment results are compared and analyzed ,and result in best recognition method -WDMVQ based on standard deviation as speaker recognition method of this system .