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http://dx.doi.org/10.14400/JDC.2015.13.1.243

Decision Tree State Tying Modeling Using Parameter Estimation of Bayesian Method  

Oh, SangYeob (Dept. of Computer Media Convergence, Gachon University)
Publication Information
Journal of Digital Convergence / v.13, no.1, 2015 , pp. 243-248 More about this Journal
Abstract
Recognition model is not defined when you configure a model, Been added to the model after model building awareness, Model a model of the clustering due to lack of recognition models are generated by modeling is causes the degradation of the recognition rate. In order to improve decision tree state tying modeling using parameter estimation of Bayesian method. The parameter estimation method is proposed Bayesian method to navigate through the model from the results of the decision tree based on the tying state according to the maximum probability method to determine the recognition model. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method error rate reduction of 1.29% compared with baseline model, which is slightly better performance than the existing approach.
Keywords
HMM; Vocabulary Recognition; Bayesian; Decision Tree; Tying Modeling;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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