Pattern Matching Research Of Voiceprint Identification In Wechat

Recently, Wechat has occupied an increasingly important position in mobile applications,it has become the first choice in people’s social activities,its an applications to provide a free instant messaging services for intelligent terminal, so people can communicate with operators and operate the system platform through Wechat.Besides,through network,they can also send voice,free text,pictures and video to communicate each other and share their life.But with the advent of the information age, computer and communication technology and other high technology can be seen everywhere in our daily life,it makes our life more convenient and colorful, however, it has also caused some problems. For instance,all cards must be carried-on,and the complex password is too difficult to remember,and if the card is lost and the password is stolen,it can bring security risks and losses to people and also the Wechat itself.Otherwise,if the phone lost into others,they imitate the voice to defrauding others trust to implement malpractice. So privacy issues need to be addressed urgently. While Biometric Identification is the combination of Biology and Information Technology, make the Identification Authentication more secure,convient and without needing memory,that help us to solve this problem.And it s mainly identified by inherent physiological and behavioral characteristics to realize the distinguish of indentity. Voiceprint recognition also belong to the biological recognition, it has the advantages of accessibility, easy to use, low cost and remote manipulation,it’s the only one technology fo indentity of the remote.SO it has been widely used in the field of finance, securities, public security, military, socialsecurity, medical and other civilian security certification.And Chinese use of Voiceprint Recognition is still in the infancy, there are broad prospects for its development, then, the Wechat users can confirm others identities through voice to avoided getting damaged.This article presents an analysis on several modules of Voiceprint Recognition: preprocessing,feature extraction, pattern matching and recognition judgment. In addition, it also focus on the research for the parrern matching algorithms of Voiceprint Recognition.There are a lot of voiceprint recognition mode matching methods like dynamic time warping technique(DTW), Artificial Neural Network(ANN), hidden Markov model(HMM), Gaussian Mixture Model(GMM), etc. Because the precision of DTW is hard to aim, which lead to a low recognition rate, while ANN need a very long time for training, HMM need a large amount of calculation. Therefore, in order to improve the recognition accuracy and efficiency, this paper selects the current mainstream technology of text independent speaker recognition of Gauss mixture model(Gaussian Mixture Model, GMM) as modeling method. Through the combination of the discrete GMM, with mean and covariance matrix to express the Gauss function, so as to get the GMM. Because the Gauss mixture model GMM has better fitting characteristics of distribution of acoustic features, the GMM method based on the maximum likelihood decision has become the mainstream method of speaker recognition system. It is the Gauss probability density function of the extension, the density distribution which is able to simulate various shapes.The training phase use EM algorithm to find the set of parameters, and the pattern recognition is realized by MAP criterion. LBG algorithm is introduced to calculate the initial values of the parameters, and the design of the combined threshold decision is based on 3 methods. The paper witnesses effect experiments of different characteristic parameters respectively, the initial point and the threshold of recognition performance.,The results show that the GMM model with mean vector and covariance matrix of the model is better, when Gauss mixed number is 32 recognition rate reached the highest. LBG algorithm with high compression ratio and low distortion, more to get a good recognition effect. Combined threshold decision can reduce the false positive rate and false alarm rate, and improve the efficiency of identification.