Savyasachi Singh Computational NeuroEngineering Lab March 19, 2008

Introduction

Model ParametersModel Parameters

Assumptions

Three basic problemsThree basic problems

Evaluation ProblemEvaluation Problem

Forward AlgorithmForward Algorithm

Backward AlgorithmBackward Algorithm

Decoding ProblemDecoding Problem

Viterbi AlgorithmViterbi Algorithm

Learning ProblemLearning Problem

ML Estimation: EM algorithmML Estimation: EM algorithm

Baum Welch AlgorithmBaum Welch Algorithm

Re-estimation formulaeRe-estimation formulae

Gradient based methodGradient based method

Practical PitfallsPractical Pitfalls

Limitations

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

Typical ImplementationsTypical Implementations

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.