Statistical performance analysis of MCE/GPD learning in Gaussian classifiers and hidden Markov models [speech recognition example]

The minimum classification error, and generalized probabilistic descent (MCE/GPD) algorithm is a very popular and powerful framework for building classifiers with many practical applications. This paper first presents a theoretical analysis of MCE/GPD for a 2-class Gaussian classification problem. We show that the algorithm converges to the optimum classifier, and that further iterations lead to an increase in the inter-class distance which increases the classifier variance without contributing to lowering its error. The theoretical results are supported by simulations for Gaussian classifiers, and generalize to a hidden Markov model speech recognition problem.