A tutorial on hidden Markov models and selected applications inspeech recognition
Rabiner, L.R.
AT&T Bell Lab., Murray Hill, NJ;
This paper appears in: Proceedings of the IEEE
Publication Date: Feb 1989
Volume: 77,
Issue: 2
On page(s): 257-286
ISSN: 0018-9219
References Cited: 66
CODEN: IEEPAD
INSPEC Accession Number: 3424909
Digital Object Identifier: 10.1109/5.18626
Posted online: 2002-08-06 16:36:58.0
Abstract
This tutorial provides an overview of the basic theory of hidden
Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and
gives practical details on methods of implementation of the theory along
with a description of selected applications of the theory to distinct
problems in speech recognition. Results from a number of original
sources are combined to provide a single source of acquiring the
background required to pursue further this area of research. The author
first reviews the theory of discrete Markov chains and shows how the
concept of hidden states, where the observation is a probabilistic
function of the state, can be used effectively. The theory is
illustrated with two simple examples, namely coin-tossing, and the
classic balls-in-urns system. Three fundamental problems of HMMs are
noted and several practical techniques for solving these problems are
given. The various types of HMMs that have been studied, including
ergodic as well as left-right models, are described
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