Minimum cross-entropy adaptation of hidden Markov models

Adaptation techniques that benefit from distribution correlation are important in practical situations having sparse adaptation data. The so called extended MAP (EMAP) algorithm provides an optimal, though expensive, solution. In this article we start from EMAP, and propose an approximate optimisation criterion, based on maximising a set of local densities. We then obtain expressions for these local densities based on the principle of minimum cross-entropy (MCE). The solution to the MCE problem is obtained using an analogy with MAP estimation, and avoids the use of complex numerical procedures, thus resulting in a simple adaptation algorithm. The implementation of the proposed method for the adaptation of HMMs with mixture Gaussian densities is discussed, and its efficiency is evaluated on an alphabet recognition task