%A Dr Dimitri Kanevsky
%T EXTENDED BAUM TRANSFORMATIONS FOR GENERAL FUNCTIONS, II
%X The discrimination technique for estimating the parameters of Gaussian mixtures that is based on the Extended Baum transformations (EB) has had significant impact on the speech recognition community.  The proof that definitively shows that these transformations increase the value of an objective function with iteration (i.e., so-called "growth transformations") was presented by the author two years ago  for a diagonal Gaussian mixture densities. In this paper this proof is extended to a multidimensional multivariate  Gaussian mixtures. The proof presented in the current paper is based on the linearization process and the explicit growth estimate for linear forms of Gaussian mixtures. 
%D 2005
%K Multidimensional Multivariate Gaussuan Mixture, Extended Baum-Welch transformations, discriminative training, estimation of statistical parameters, maximum mutual informaiton estimation
%I IBM
%L cogprints5058

<script src="//archive-bar.soton.ac.uk/archive-bar.js"></script>