A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces
Open Access
- 1 May 2001
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Speech and Audio Processing
- Vol. 9 (4), 411-416
- https://doi.org/10.1109/89.917686
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
This publication has 7 references indexed in Scilit:
- Sufficiency, classification, and the class-specific feature theoremIEEE Transactions on Information Theory, 2000
- Class-specific feature sets in classificationIEEE Transactions on Signal Processing, 1999
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov ChainsAT&T Technical Journal, 1985
- Multiple Time SeriesWiley Series in Probability and Statistics, 1970
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov ChainsThe Annals of Mathematical Statistics, 1970
- The Joint Distribution of Serial Correlation CoefficientsThe Annals of Mathematical Statistics, 1949