Supervised Hidden Markov Model learning using the state distribution oracle

Document Type

Conference Proceeding

Publication Date

12-1-2004

Abstract

Hidden Markov Models (HMMs) are probabilistic models with applications across a large number of fields, most prominently Speech Recognition and Computational Biology. In this paper, we propose a polynomial-time algorithm for learning the parameters of a first order HMM by using a state distribution probability (SD) oracle. The SD oracle provides the learning algorithm with the state distribution corresponding to a query string in the target model. The SD oracle is necessary for efficient learning in the sense that the consistency problem for HMMs, where a training set of state distribution vectors such as those supplied by the SD oracle is used but without the ability to query on specific strings, is NP-complete. The algorithm proposed here is an extension to an algorithm described by Tzeng for learning Probabilistic Automata (PA) using the SD oracle.

Publication Source (Journal or Book title)

2004 IEEE Conference on Cybernetics and Intelligent Systems

First Page

240

Last Page

244

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