LECTURE 24: HMM TRAINING

• Objectives:
  
  
  • Pose parameter reestimation as an unsupervised learning problem
    
    
  • Introduce the Baum-Welch (Forward-Backward) Algorithm
    
    
  • Apply EM algorithm to reestimate parameters
    
    
  • Describe Viterbi training

This lecture combines material from the course textbook:

X. Huang, A. Acero, and H.W. Hon, Spoken Language Processing - A Guide to Theory, Algorithm, and System Development, Prentice Hall, Upper Saddle River, New Jersey, USA, ISBN: 0-13-022616-5, 2001.

and information found in most standard speech textbooks:

J. Deller, et. al., Discrete-Time Processing of Speech Signals, MacMillan Publishing Co., ISBN: 0-7803-5386-2, 2000.