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Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure

  • Kim Schmidt
  • Karl Heinz Hoffmann
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

Abstract

Hidden Markov Models (HMMs) are widely used in speech and handwriting recognition, behavior prediction in traffic, time series analysis, biostatistics, image and signal processing, and many other fields. For some applications in those real world problems, a-priori knowledge about the structure of the HMM is available. For example the shape of the state transition matrix and/or the observation matrix might be given. We might know that some entries in these matrices are equal and others are zero. For training such a model, we have two options: use the common Baum Welch Algorithm (BWA) and enforce the given structure after training or modify the BWA to enforce it during training. This paper shows several approaches for modifying the BWA and compares the results of all training methods.

Keywords

Hidden Markov Model HMM Baum Welch Algorithm Multiple sequence learning A-priori knowledge 

Notes

Acknowledgments

This research was supported by the European Social Fund and the Free State of Saxony under Grant No. 100269974.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chemnitz University of TechnologyChemnitzGermany

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