Training approach for hidden Markov models

Abstract

The authors propose a new training approach based on maximum model distance (MMD) for HMMs. MMD uses the entire training set to estimate the parameters of each HMM, while the traditional maximum likelihood (ML) only uses those data labelled for the model. Experimental results showed that significant error reduction can be achieved through the proposed approach. In addition, the relationship between MMD and corrective training was discussed, and we have proved that the corrective training is a special case of the MMD approach.