TRAINING MANY SMALL HIDDEN MARKOV MODELS
This paper describes research in progress on two quite different ways of training systems that are composed of many small Hidden Markov Models (HMM’s). The first is a purely discriminative method in which all of the parameters of all the HMM’s are adjusted to optimize classification performance. The second is an unsupervised method in which many little HMM’s are used to model the probability density of a single set of sequences.