## Best-first Model Merging for Hidden Markov Model Induction (1994)

Citations: | 93 - 7 self |

### BibTeX

@TECHREPORT{Stolcke94best-firstmodel,

author = {Andreas Stolcke and Stephen M. Omohundro},

title = {Best-first Model Merging for Hidden Markov Model Induction},

institution = {},

year = {1994}

}

### Years of Citing Articles

### OpenURL

### Abstract

This report describes a new technique for inducing the structure of Hidden Markov Models from data which is based on the general `model merging' strategy (Omohundro 1992). The process begins with a maximum likelihood HMM that directly encodes the training data. Successively more general models are produced by merging HMM states. A Bayesian posterior probability criterion is used to determine which states to merge and when to stop generalizing. The procedure may be considered a heuristic search for the HMM structure with the highest posterior probability. We discuss a variety of possible priors for HMMs, as well as a number of approximations which improve the computational efficiency of the algorithm. We studied three applications to evaluate the procedure. The first compares the merging algorithm with the standard Baum-Welch approach in inducing simple finitestate languages from small, positive-only training samples. We found that the merging procedure is more robust and accurate, part...