Abstract
We detail an algorithm that transforms any higher order hidden Markov model (HMM) to an equivalent first order HMM. This makes it possible to process higher order HMMs with standard techniques applicable to first order models. Based on this equivalence, a fast incremental algorithm is developed for training higher order HMMs from lower order approximations, thereby avoiding the training of redundant parameters. This makes training of high order HMMs practical for many applications.
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