A Novel Method for Decoding Any High-Order Hidden Markov Model

Abstract EN

This paper proposes a novel method for decoding any high-order hidden Markov model.

First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation.

Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model.

Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model.

This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.