A trust region based optimization for maximum mutual information estimation of HMMS in speech recognition

Abstract

In this paper, we present a new optimization method for MMIE-based discriminative training of HMMs in speech recognition. In our method, the MMIE training of Gaussian mixture HMMs is formulated as a so-called trust region problem, where a quadratic objective function is minimized under a spherical constraint, so that an efficient global optimization method for the trust region problem can be used to solve the MMIE training problem of HMMs. Experimental results on the WSJ0 Nov'92 evaluation task demonstrate that the trust region based optimization significantly outperforms the conventional EBWmethod in terms of optimization convergence behavior as well as speech recognition performance. It has been observed that the trust region method achieves up to 23.3% relative recognition error reduction over a well-trained MLE system while the EBW method gives only 13.3% relative error reduction.