Research And Implementation On Speaker Recognition System Based On GMM

Speaker Recognition is also known as Voiceprint Identification, of which the purpose is to indentify or verify the speaker based on the voice.With the rapid development of network information technology, the digitalization, recessivation and facilitation of identity authentication has become more and more important. As a biological authentication technology, Speaker Recognition has wide application prospects in many fields such as surveillance, authentication, investigation and finance security and become a hot spot in the research on speech signal processing. The key technologies of Speaker Recognition are feature extraction and pattern matching currently. On the condition that research the key algorithm of the current speaker recognition, this paper study the method of feature extraction based on acoustic performance, the method of pattern matching base on template matching and probability-statistics.Analyze and verify Speaker Recognition System base on VQ. Thoroughly, study and design of Text-independent Speaker Recognition System based on GMM.The concrete content is as follows:(1) Summarize status of development, the study hotspot and difficulty in speaker recognition technology. Analyze and discuss the existing main algorithm in speaker recognition.(2) Study voice signal processing and spectral subtraction method of speech enhancement algorithms in speaker recognition system of front end process, improves spectral subtraction method. The experiment shows that the robustness of speaker recognition system is improved in noisy environment. Research the fundamental principle of feature extraction of speaker recognition. Realize parameter extraction process of pitch, LPCC, MFCC and its difference by simulation.(3) On the basis of analyzing the fundamental principle of VQ, the algorithm of LBG and mode initialization in VQ, Design and Implementation of speaker recognition system based on VQ. Establish of training model parameters and the process of recognizing parameters matching. Analyze the performance of speaker recognition system in different model parameters and duration of speech samples by experiments.(4) To improve the recognition rate and the stability of the system, research the algorithm of expectation maximization (EM) for parameter estimation, model parameter initialization, the process of training parameters and recognizing parameters in GMM, and complete simulation and experiment. Analyze the performance of system in different model parameter, methods of feature extraction, duration of speech samples, various SNR.(5)Analyze the open-set speaker recognition, the rule and method of getting threshold value in speaker verification. A method of speaker identification followed speaker verification in open-set speaker recognition is presented. Solve "rejection problems" for pretenders.Finally, analyses and compares the performance of open-set speaker recognition based on VQ and GMM.