Simplified Training Algorithms for Hierarchical Hidden Markov Models

Authors:
Nobuhisa Ueda;Taisuke Sato
Affiliations:
-;-
Venue:
DS '01 Proceedings of the 4th International Conference on Discovery Science
Year:
2001

Citing 5
Cited 1

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Abstract

We present a simplified EM algorithm and an approximate algorithm for training hierarchical hidden Markov models (HHMMs), an extension of hidden Markov models. The EM algorithm we present is proved to increase the likelihood of training sentences at each iteration unlike the existing algorithm called the generalized Baum-Welch algorithm. The approximate algorithm is applicable to tasks like robot navigation in which we observe sentences and train parameters simultaneously. These algorithms and their derivations are simplified by making use of stochastic context-free grammars.