In this paper we aim to improve the performance of Gaussian Mixture Model (GMM) classifier using Impostor model parameters for a closed set Speaker Identification task. We propose a novel method of speaker model training which uses the parameters of an Impostor Model to discriminatively train, in order to improve the performance of the GMM based classifier. This is unlike conventional techniques which use Minimum Classification Error (MCE) criterion which uses the ensemble of speaker data, instead. An experimental comparison between the conventional GMM and the proposed technique is presented.