A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 39, Issue 1, 2009, Pages 182-197

  Huda, Shamsul ^a   Yearwood, John ^a   Togneri, Roberto ^b  

^a UNIVERSITY OF BALLARAT   (Australia)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

Constraint based evolutionary algorithm (EA); Expectation maximization (EM); Fusion strategies; Hidden Markov model (HMM); Hybrid algorithm; Lagrange multiplier (LM); Signal modeling and classification; Speech recognition

Indexed keywords

CIRCUIT THEORY; COERCIVE FORCE; CONSTRAINED OPTIMIZATION; ESTIMATION; FREQUENCY MULTIPLYING CIRCUITS; HIDDEN MARKOV MODELS; LAGRANGE MULTIPLIERS; MAXIMUM PRINCIPLE; OBJECT RECOGNITION; REMELTING; SPEECH ANALYSIS; SPEECH RECOGNITION;

CONSTRAINT-BASED EVOLUTIONARY ALGORITHM (EA); EXPECTATION MAXIMIZATION (EM); FUSION STRATEGIES; HIDDEN MARKOV MODEL (HMM); HYBRID ALGORITHM; SIGNAL MODELING AND CLASSIFICATION;

EVOLUTIONARY ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; AUTOMATIC SPEECH RECOGNITION; HUMAN; METHODOLOGY; NORMAL DISTRIBUTION; PROBABILITY; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SPEECH RECOGNITION SOFTWARE;

EID: 61549139402     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.2004051     Document Type: Article

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