HMM training based on CV-EM and CV Gaussian mixture optimization

Abstract

A combination of the cross-validation EM (CV-EM) algorithm and the cross-validation (CV) Gaussian mixture optimization method is explored. CV-EM and CV Gaussian mixture optimization are our previously proposed training algorithms that use CV likelihood instead of the conventional training set likelihood for robust model estimation. Since CV-EM is a parameter optimization method and CV Gaussian mixture optimization is a structure optimization algorithm, these methods can be combined. Large vocabulary speech recognition experiments are performed on oral presentations. It is shown that both CV-EM and CV Gaussian mixture optimization give lower word error rates than the conventional EM, and their combination is effective to further reduce the word error rate.