Science & Engineering Research Support soCiety (SERSC)
In speaker Identification systems both parametric and nonparametric probability modeling is used. The Gaussian model is the basic parametric model that is used and this model is the basis of other sophisticated and it can be performed in a completely text independent situation. However, it sounds efficient to speaker identification application, but it results long time processing in practice. In this paper, the authors propose a decision function by using Vector Quantization (VQ) techniques to decrease the training model for GMM (Gaussian Mixture Model) in order to reduce the processing time.