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Abstract: We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in language, handwriting and speech. We seek a systematic unsupervised approach to the modeling of such structures. By extendingthe standard forward-backward(BaumWelch) algorithm, we derive an efficient... (Update)
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BibTeX entry: (Update)
Shai Fine, Yoram Singer, and Naftali Tishby. The Hierarchical Hidden Markov Model: Analysis and Applications. Machine Learning, 32(1), July 1998. http://citeseer.ist.psu.edu/fine98hierarchical.html More
@article{ fine98hierarchical,
author = "Shai Fine and Yoram Singer and Naftali Tishby",
title = "The Hierarchical Hidden Markov Model: Analysis and Applications",
journal = "Machine Learning",
volume = "32",
number = "1",
pages = "41-62",
year = "1998",
url = "citeseer.ist.psu.edu/fine98hierarchical.html" }
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