An Approach for Estimating Parameters in Gaussian Mixture Model Based on Maximum Cross Entropy
基于最大交叉熵估计高斯混合模型参数的方法

The traditional approach for estimating parameters in Gaussian mixture models (GMM) based on maximum likelihood is a kind of unsupervised learning method, its shortage is that the parameters in GMM are derived only by the training samples in one class without taking the effect of sample distributions of other classes into account, hence, its recognition is usually not ideal. In this paper, an approach is presented for estimating parameters in GMM based on the maximum cross entropy of different classes, this method takes the discriminations of samples in different classes into account. To increase the possibility of obtaining the global optimal solution, this paper proposes an approach for estimating the optimal parameters in GMM based on evolutionary programming. An experiment has been carried out using the method for the text-independent speaker recognition, the results have shown that the recognition accuracy is higher than that of the traditional approach. The method has also fast convergent speed.