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EEL 6586-Automatic Speech Processing Hidden Markov Models for Speech Recognition

EEL 6586-Automatic Speech Processing Hidden Markov Models for Speech Recognition. Savyasachi Singh Computational NeuroEngineering Lab March 19, 2008. Introduction. Model Parameters. Assumptions. Three basic problems. Evaluation Problem. Forward Algorithm. Backward Algorithm.

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EEL 6586-Automatic Speech Processing Hidden Markov Models for Speech Recognition

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  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.

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