Chapter 5. Hidden Markov Models

  1. Saeed V. Vaseghi Professor

Published Online: 18 MAR 2009

DOI: 10.1002/9780470740156.ch5

Advanced Digital Signal Processing and Noise Reduction, Fourth Edition

Advanced Digital Signal Processing and Noise Reduction, Fourth Edition

How to Cite

Vaseghi, S. V. (2008) Hidden Markov Models, in Advanced Digital Signal Processing and Noise Reduction, Fourth Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470740156.ch5

Author Information

  1. Department of Electronics & Computer Engineering, Brunel University, London, UK

Publication History

  1. Published Online: 18 MAR 2009
  2. Published Print: 6 FEB 2008

ISBN Information

Print ISBN: 9780470754061

Online ISBN: 9780470740156

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Keywords:

  • Hidden Markov models (HMMs);
  • non-stationary first-order autoregressive (AR) process;
  • three-state ergodic HMM structure;
  • five-state left–right HMM speech model;
  • mixture Gaussian probability density function;
  • HMMs training Baum–Welch method;
  • Hidden Markov models - speech recognition, image recognition and signal restoration;
  • Viterbi algorithm;
  • HMMs in speech and noise modelling

Summary

This chapter contains sections titled:

  • Statistical Models for Non-Stationary Processes

  • Hidden Markov Models

  • Training Hidden Markov Models

  • Decoding Signals Using Hidden Markov Models

  • HMMs in DNA and Protein Sequences

  • HMMs for Modelling Speech and Noise

  • Summary

  • Bibliography