Abstract
Since mixing degree of the traditional Gaussian mixture model is constant, and it does not conform to the characteristics of speaker feature distribution. In this case, the problems of fitting deficiencies or fitting excessive exist and it will affect the speaker recognition rate. A new algorithm to improve the Gaussian mixture model was proposed and it was applied to speaker recognition. The algorithm adaptively adjusted the weight, mean and covariance of the Gaussian component according to distribution characteristics of the speaker's characteristic parameter. It made the improved Gaussian mixture model could better fit distribution features of the characteristic parameters of the speaker. Thus the speaker recognition rate was improved. Experiments showed that the speaker recognition rate of improved Gaussian mixture model was higher than the traditional Gaussian mixture model.
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