1. EEL 6586-Automatic Speech ProcessingHidden Markov Models for Speech Recognition Savyasachi Singh Computational NeuroEngineering Lab March 19, 2008

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) end 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. end 5. Tabulate confusion matrix.