1. Savyasachi Singh Computational NeuroEngineering Lab March 19, 2008

2. Introduction

3. Model ParametersModel Parameters

4. Assumptions

5. Three basic problemsThree basic problems

6. Evaluation ProblemEvaluation Problem

7. Forward AlgorithmForward Algorithm

8. Backward AlgorithmBackward Algorithm

9. Decoding ProblemDecoding Problem

10. Viterbi AlgorithmViterbi Algorithm

11. Learning ProblemLearning Problem

12. ML Estimation: EM algorithmML Estimation: EM algorithm

13. Baum Welch AlgorithmBaum Welch Algorithm

14. Re-estimation formulaeRe-estimation formulae

15. Gradient based methodGradient based method

16. Practical PitfallsPractical Pitfalls

17. Limitations

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

19. Typical ImplementationsTypical Implementations

20. HW 4 part c pseudocodeHW 4 part c pseudocode 1.Chop speech signal into frames and extract features. (preferably MFCC) 2.Choose HMM parameters N, M, cov. type, A etc. 3.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 4.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.