Text-independent Speaker Recognition

Due to its special merits of flexibility, economy and accuracy, speaker recognition technology has a broad application future in biometrics security field. However, speaker recognition techniques have performed well under ideal conditions. There are still many problems when we want to apply speaker recognition to real applications, One most cause is the long computational time of training a speaker model or test an utterance, and in the recognition stage, the test utterance must match the every speaker mode. This makes real-time implementation very hard and expensive. Thus the problem of improving train time and recognition time has turned into the most active research filed without deteriorating recognition performance.Support vector machine technology is one of statistics learning theories. It has a very great advantage while dealing the samples with nonlinear and multidimensional problems on the basis of the mode categorized method of the structure risk with minimizing principle. So there are good results on speaker recognition based on speech signal samples. However, training a speaker SVM model consumes large memory and long computing time with all the speech parameters, and in the recognition stage, the test utterance must match the every speaker mode. This thesis has systematically investigated existing works from other colleagues, and proposed some novel approaches:1,In the speaker recognition, there are some major difficulties that confront large extractive feature data, which will consumes large memory and long computing time to training SVM with all speech parameters. This paper proposes a speaker identification method based on multi-reduced support vector machine (MRSVM) to reduce training time and the memory size for SVM. Viz. PCA and kernel-based fuzzy clustering are used to reduce the dimensions and amounts of training data respectively, the experiment results show that the training data, time and storage can be reduced remarkably by using our method without deteriorating recognition performance, and the system has better robustness.2,To save the recognition time of speaker identification, this paper proposes a novel hierarchical speaker identification(HSI) system based on MRSVM and PCA classifier. PCA classifier come true easy and fast because it needn't to train, so that the PCA classifier is used to get a coarse judge by a fast scan all registered speakers. And the selected MRSVM models are used to get a final decision by the result of the first judge. Experiments show that HSI have the similar identification performance compared with traditional method, but the identification velocity is improved greatly. And the system is easy to add and delete a new speaker...