Speaker Recognition Based On Neural Network And Hmm

Speaker recognition aims to identify different identity, the recognition process is to select a certain sound features firstly, and then use some of the model algorithm for each speaker to establish unique template library for each template matching, and get the best matching results at last. In the field of speaker recognition, various characteristic parameters which are widely used have advantages and disadvantages, and the recognition results are not very satisfactory, since a long time, characteristic parameters which can be able to characterize the speakers individuality completely has not been found. This article discusses several common feature parameters and the algorithm of the model, and introducing a new wavelet feature parameters and the improved algorithm of neural network to make up a speaker recognition system.This paper introduces a kind of common characteristic parameter firstly, Mel cepstrum coefficient (MFCC), the parameter is based on cepstrum parameters, however, in describing the speaker personality characteristics, distinguishing ability of the parameter has some lack, therefore this article extracts a wavelet MFCC features using cepstrum principle and wavelet transform; moreover, in the algorithm of the model, the paper analyses initial importance of the hidden Markov model and describes the initialization method of the K order mean clustering algorithm which is used generally, while introducing self organizing neural network clustering algorithm, and make the comparison with the K order mean clustering algorithm in the process of training convergence aspects.Experimental results show that, using the wavelet MFCC features can greatiy reduce the number of its calculation, and the system recognition rate reached94.4%, compared with87.5%when used MFCC features, the recognition rate is improved by about7%; at the same time, in the experiment using self-organizing feature map neural network and self-organizing competitive neural network to improve K order mean clustering algorithm, based on the training iterations and the recognition rate obtained by different characteristic parameters of different speakers through different algorithms,we can analyse the advantages and disadvantages of different algorithms and their existing problems.