Factorization of Gaussian mixtures densities for hidden Markov models

We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models. We derive the reestimation formulas for estimating the factorized parameters by the expectation maximization (EM) algorithm. As shown in the experiments, the proposed model is able to overcome the overfitting problem when sufficient training samples are not available.