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2004/11/161 A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition LAWRENCE R. RABINER, FELLOW, IEEE Presented by: Chi-Chun Hsia
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2004/11/162 Markov Chain
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2004/11/163 Markov Chain
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2004/11/164 Markov Chain And then, what does “hidden” means? It results in a Geometric Distribution
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2004/11/165 Extension to Hidden Markov Model
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2004/11/166 Extension to Hidden Markov Model
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2004/11/167 Elements of an HMM
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2004/11/168 The Three Basic Problems
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2004/11/169 Solution to Problem 1
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2004/11/1610 Solution to Problem 1
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2004/11/1611 Forward-Backward Procedure
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2004/11/1612 Forward-Backward Procedure
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2004/11/1613 Forward-Backward Procedure
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2004/11/1614 Forward-Backward Procedure
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2004/11/1615 Solution to Problem 2
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2004/11/1616 Solution to Problem 2
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2004/11/1617 Viterbi Algorithm
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2004/11/1618 Viterbi Algorithm
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2004/11/1619 Solution to Problem 3
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2004/11/1620 Solution to Problem 3
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2004/11/1621 EM Algorithm for HMM X.D. HUANG, Y. ARIKI, M.A. JACK HIDDEN MARKOV MODELS FOR SPEECH RECOGNITION EDINBURGH UNIVERSITY PRESS
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2004/11/1622 Types of HMMs
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2004/11/1623 Continuous Type HMMs
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2004/11/1624 Autoregressive HMMs
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2004/11/1625 Optimization Criterion Maximum Likelihood (ML) Maximum Mutual Information (MMI) Minimum Discrimination Information (MDI) Minimum Classification Error (MCE) Chang.
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2004/11/1626 Implementation Issues for HMMs Scaling Multiple Observation Sequences Initial Estimates of HMM Parameters Effect of Insufficient Training Data Choice of Model
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2004/11/1627 Scaling
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2004/11/1628 Scaling
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2004/11/1629 Scaling And so on and on and on and on……………..
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2004/11/1630 Multiple Observation Sequences
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2004/11/1631 Initial Estimates of HMM Parameters
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2004/11/1632 Effect of Insufficient Training Data
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2004/11/1633 Choice of Model
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