1. Computational NeuroEngineering Lab
EEL 6586-Automatic Speech Processing Hidden Markov Models for Speech Recognition
Savyasachi Singh
Computational NeuroEngineering Lab
March 28, 2007

2. Introduction

3. Model Parameters

4. Assumptions

5. Three basic problems

6. Evaluation Problem

7. Forward Algorithm

8. Backward Algorithm

9. Decoding Problem

10. Viterbi Algorithm

11. Learning Problem

12. ML Estimation: EM algorithm

13. Baum Welch Algorithm

14. Re-estimation formulae

15. Gradient based method

16. Practical Pitfalls

17. Limitations

18. Isolated Word Recognition
HMM
Word 1
HMM
Word 2
FEATURE
EXTRACTION
SELECT
MAXIMUM
HMM
Word 3
HMM
Word K

19. Typical Implementations

20. HW 4 part c pseudocode
Chop speech signal into frames and extract features. (preferably MFCC)
Choose HMM parameters N, M, cov. type, A etc.
Start learning procedure for train set
for each word repeat following steps
for each state
Initialize GMM’s and get parameters (use mixgauss_init.m)
end
Train HMM with EM (use mhmm_em.m)
Start testing procedure for test set
for each test utterance
Compare with all trained models and get log likelihood (score) using forward backward algorithm. (use mhmm_logprob.m)
Select model with highest score as recognized word.
5. Tabulate confusion matrix.