Hidden Markov models: a guided tour
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on, page 7 -13 vol.1. (April 1988)DOI: 10.1109/ICASSP.1988.196495
Abstract
Hidden Markov modeling is a probabilistic technique for the study
of time series. Hidden Markov theory permits modeling with any of the
classical probability distributions. The costs of implementation are
linear in the length of data. Models can be nested to reflect
hierarchical sources of knowledge. These and other desirable features
have made hidden Markov methods increasingly attractive for problems in
language, speech and signal processing. The basic ideas are introduced
by elementary examples in the spirit of the Polya urn models. The main
tool in hidden Markov modeling is the Baum-Welch (or forward-backward)
algorithm for maximum likelihood estimation of the model parameters.
This iterative algorithm is discussed both from an intuitive point of
view as an exercise in the art of counting and from a formal point of
view via the information-theoretic Q-function. Selected examples drawn
from the literature illustrate how the Baum-Welch technique places a
rich variety of computational models at the disposal of the researcher
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