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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| Published in: | Artificial intelligence in engineering Vol. 12; no. 3; pp. 243 - 252 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Elsevier Ltd
01.07.1998
Elsevier |
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| ISSN: | 0954-1810 |
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| Abstract | 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 state-dependent quantized vector which is regarded as a variant of a representative vector at a state and its own codeword distribution, and the output distribution is derived by these state-dependent sources which will exist at a state. In addition, entropy-based feature-parameter weighting is proposed to reflect the different importance of each feature-parameter in a state, and the fuzzy function is applied to transform an entropy value into a feature-parameter weighting factor. From experiments, we found that proposed methods have shown an improvement of 5.6%, which indicates the effectiveness of proposed models in the robust estimation of output probabilities for DHMMs. |
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| AbstractList | 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 state-dependent quantized vector which is regarded as a variant of a representative vector at a state and its own codeword distribution, and the output distribution is derived by these state-dependent sources which will exist at a state. In addition, entropy-based feature-parameter weighting is proposed to reflect the different importance of each feature-parameter in a state, and the fuzzy function is applied to transform an entropy value into a feature-parameter weighting factor. From experiments, we found that proposed methods have shown an improvement of 5.6%, which indicates the effectiveness of proposed models in the robust estimation of output probabilities for DHMMs. |
| Author | Yun, Sung Jin Choi, Hwan Jin Oh, Yung Hwan |
| Author_xml | – sequence: 1 givenname: Hwan Jin surname: Choi fullname: Choi, Hwan Jin email: hjchoi@bulsai.kaist.ac.kr – sequence: 2 givenname: Sung Jin surname: Yun fullname: Yun, Sung Jin – sequence: 3 givenname: Yung Hwan surname: Oh fullname: Oh, Yung Hwan |
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| Keywords | 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 |
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Commun. doi: 10.1109/TCOM.1980.1094577 – volume: 60 start-page: 681 year: 1972 ident: 10.1016/S0954-1810(97)00026-5_BIB18 article-title: Discrete representation of signals – volume: 34 start-page: 52 year: 1986 ident: 10.1016/S0954-1810(97)00026-5_BIB9 article-title: Speaker independent isolated word recognition using dynamic features of speech spectrum publication-title: IEEE Trans. ASSP doi: 10.1109/TASSP.1986.1164788 – volume: 64 start-page: 1211 year: 1985 ident: 10.1016/S0954-1810(97)00026-5_BIB4 article-title: Recognition of isolated digits using hidden Markov models with continuous mixture densities publication-title: AT&T Tech. J. doi: 10.1002/j.1538-7305.1985.tb00272.x |
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| SubjectTerms | 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 |
| Title | Robust estimation of discrete hidden Markov model parameters using the entropy-based feature-parameter weighting and source-quantization modeling |
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