Abstract

AbstractThis paper considers an extension of the hidden Markov model, called the hierarchical hidden Markov model, and proposes an EM algorithm and an approximate algorithm. The EM algorithm proposed in this paper differs from the existing training algorithm, which is called the Baum–Welch algorithm, and guarantees that the likelihood is always increased by updating the parameters. The approximate algorithm has the advantage that it can be applied to problems in which observation of the training sentence and training of the parameters proceed in parallel. These algorithms and their derivations are simplified, compared to the existing training algorithm, by using stochastic context‐free grammar. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(5): 59–69, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10172

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