Improved learning for hidden Markov models using penalized training

In this paper we investigate the performance of penalized variants of the forwards-backwards algorithm for training Hidden Markov Models. Maximum likelihood estimation of model parameters can result in over-fitting and poor generalization ability. We discuss the use of priors to compute maximum a posteriori estimates and describe a number of experiments in which models are trained under different conditions. Our results show that MAP estimation can alleviate over-fitting and help learn better parameter estimates.