A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces

Author

Baggenstoss, Paul M.

Author_Institution

Naval Underwater Syst. Center, Newport, RI, USA

Volume

9

Issue

4

fYear

2001

fDate

5/1/2001 12:00:00 AM

Firstpage

411

Lastpage

416

Abstract

We derive an algorithm similar to the well-known Baum-Welch (1970) algorithm for estimating the parameters of a hidden Markov model (HMM). The new algorithm allows the observation PDF of each state to be defined and estimated using a different feature set. We show that estimating parameters in this manner is equivalent to maximizing the likelihood function for the standard parameterization of the HMM defined on the input data space. The processor becomes optimal if the state-dependent feature sets are sufficient statistics to distinguish each state individually from a common state

Keywords

hidden Markov models; parameter estimation; speech recognition; statistical analysis; HMM; hidden Markov models; input data space; maximum likelihood function; modified Baum-Welch algorithm; multiple observation spaces; observation PDF; parameter estimation; processor; state-dependent feature sets; sufficient statistics; Error analysis; Estimation error; Helium; Hidden Markov models; Maximum likelihood estimation; Parameter estimation; Performance loss; State estimation; Statistics; Stochastic processes;

fLanguage

English

Journal_Title

Speech and Audio Processing, IEEE Transactions on

Publisher

ieee

ISSN

1063-6676

Type

jour

DOI

10.1109/89.917686

Filename

917686