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A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition

A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition LAWRENCE R. RABINER, FELLOW, IEEE Presented by: Chi-Chun Hsia. Markov Chain. Markov Chain. Markov Chain. It results in a Geometric Distribution. And then, what does “hidden” means?.

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A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition

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  1. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition LAWRENCE R. RABINER, FELLOW, IEEE Presented by: Chi-Chun Hsia 1

  2. Markov Chain 2

  3. Markov Chain 3

  4. Markov Chain It results in a Geometric Distribution And then, what does “hidden” means? 4

  5. Extension to Hidden Markov Model 5

  6. Extension to Hidden Markov Model 6

  7. Elements of an HMM 7

  8. The Three Basic Problems 8

  9. Solution to Problem 1 9

  10. Solution to Problem 1 10

  11. Forward-Backward Procedure 11

  12. Forward-Backward Procedure 12

  13. Forward-Backward Procedure 13

  14. Forward-Backward Procedure 14

  15. Solution to Problem 2 15

  16. Solution to Problem 2 16

  17. Viterbi Algorithm 17

  18. Viterbi Algorithm 18

  19. Solution to Problem 3 19

  20. Solution to Problem 3 20

  21. EM Algorithm for HMM X.D. HUANG, Y. ARIKI, M.A. JACK HIDDEN MARKOV MODELS FOR SPEECH RECOGNITION EDINBURGH UNIVERSITY PRESS 21

  22. Types of HMMs 22

  23. Continuous Type HMMs 23

  24. Autoregressive HMMs 24

  25. Optimization Criterion Maximum Likelihood (ML) Maximum Mutual Information (MMI) Minimum Discrimination Information (MDI) Minimum Classification Error (MCE) Chang. 25

  26. Implementation Issues for HMMs • Scaling • Multiple Observation Sequences • Initial Estimates of HMM Parameters • Effect of Insufficient Training Data • Choice of Model 26

  27. Scaling 27

  28. Scaling 28

  29. Scaling And so on and on and on and on…………….. 29

  30. Multiple Observation Sequences 30

  31. Initial Estimates of HMM Parameters 31

  32. Effect of Insufficient Training Data 32

  33. Choice of Model 33

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