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http://dx.doi.org/10.14400/JDC.2015.13.8.295

Noise Removal using a Convergence of the posteriori probability of the Bayesian techniques vocabulary recognition model to solve the problems of the prior probability based on HMM  

Oh, Sang-Yeob (Dept. of Computer Media Convergence, Gachon University)
Publication Information
Journal of Digital Convergence / v.13, no.8, 2015 , pp. 295-300 More about this Journal
Abstract
In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.
Keywords
Bayesian Method; Vocabulary Recognition; Recognition Model; Noise Removal; Recognition Rate;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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