A Modified Baum-Welch Algorithm for Hidden Markov Models with Multiple Observation Spaces

Abstract

In this paper, we derive an algorithm similar to the well-known Baum-Welch 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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2001
Accession Number
ADA495130

Entities

People

  • Paul Baggenstoss

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Background Noise
  • Dimensionality Reduction
  • Feature Selection
  • Gaussian Noise
  • Hidden Markov Models
  • Machine Learning
  • Markov Models
  • Markov Processes
  • Models
  • Noise
  • Observation
  • Probabilistic Models
  • Probability
  • Random Variables
  • Signal Processing
  • Statistics

Readers

  • Mathematical Modeling and Probability Theory.
  • Speech Processing/Speech Recognition.
  • Statistical inference.

Technology Areas

  • Space