Finding the most likely upper level state sequence for hierarchical HMMs

Authors:
Akira Hayashi;Kazunori Iwata;Nobuo Suematsu
Affiliations:
Faculty of Information Sciences, Hiroshima City University, Japan;Faculty of Information Sciences, Hiroshima City University, Japan;Faculty of Information Sciences, Hiroshima City University, Japan
Venue:
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Year:
2013

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Abstract

Computing the most likely state sequence from an observation sequence is an important problem with many applications. The generalized Viterbi algorithm, a direct extension of the Viterbi algorithm for hidden Markov models (HMMs), has been used to find the most likely state sequence for hierarchical HMMs. However, the generalized Viterbi algorithm finds the most likely whole level state sequence rather than the most likely upper level state sequence. In this paper, we propose a marginalized Viterbi algorithm, which finds the most likely upper level state sequence by marginalizing lower level state sequences. We show experimentally that the marginalized Viterbi algorithm is more accurate than the generalized Viterbi algorithm in terms of upper level state sequence estimation.