Robust estimation of discrete hidden Markov model parameters using the entropy-based feature-parameter weighting and source-quantization modeling

We propose a new variant of the discrete hidden Markov model (DHMM) in which the output distribution is estimated by state-dependent source quantizing modeling and the output probability is weighted by the entropy of each feature-parameter at a state. The state-dependent source is represented as a s...

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Vydané v:	Artificial intelligence in engineering Ročník 12; číslo 3; s. 243 - 252
Hlavní autori:	Choi, Hwan Jin, Yun, Sung Jin, Oh, Yung Hwan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.07.1998 Elsevier
Predmet:	Applied sciences Artificial intelligence Computer science; control theory; systems entropy Exact sciences and technology feature-parameter weighting fuzzy objective function hidden Markov model Learning and adaptive systems Speech and sound recognition and synthesis. Linguistics state-dependent source quantization modeling state-dependent source quantization modeling fuzzy objective function entropy hidden Markov model feature-parameter weighting Parameter estimation Expert system Quantization Entropy Discrete system Markov model Pattern recognition Experimental study Modeling Fuzzy logic System performance Speech recognition Objective function Dynamic programming Artificial intelligence Ambiguity
ISSN:	0954-1810
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