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http://dx.doi.org/10.14400/JDC.2014.12.7.273

Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization  

Oh, Sang Yeon (Dept. of Computer Media Convergence, Gachon University)
Publication Information
Journal of Digital Convergence / v.12, no.7, 2014 , pp. 273-278 More about this Journal
Abstract
In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.
Keywords
HMM; Vocabulary Recognition; Model Optimize; Bayesian; Recognition Rate;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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1 Chan-Shik Ahn, Sang-Yeob Oh. Gaussian Model Optimization using Configuration Thread Control In CHMM Vocabulary Recognition. The Journal of Digital Policy and Management. Vol. 10, No. 7, pp. 167-172, 2012.   과학기술학회마을
2 Chan-Shik Ahn, Sang-Yeob Oh. Echo Noise Robust HMM Learning Model using Average Estimator LMS Algorithm. The Journal of Digital Policy and Management. Vol. 10, No. 10, pp. 277-282, 2012.   과학기술학회마을
3 Chan-Shik Ahn, Sang-Yeob Oh. Efficient Continuous Vocabulary Clustering Modeling for Tying Model Recognition Performance Improvement. Journal of the Korea Society of Computer and Information. Vol. 15, No. 1, pp. 177-183, 2010.   과학기술학회마을   DOI   ScienceOn
4 Chan-Shik Ahn, Sang-Yeob Oh. CHMM Modeling using LMS Algorithm for Continuous Speech Recognition Improvement. The Journal of digital policy and management. Vol. 10, No. 11, pp. 377-382, 2012.   과학기술학회마을
5 Chan-Shik Ahn, Sang-Yeob Oh. Vocabulary Recognition Retrieval Optimized System using MLHF Model . Journal of the Korea Society of Computer and Information. Vol. 14, No. 10, pp. 217-223, 2009.   과학기술학회마을
6 Y. Shao, S. Srinivasan, Z. Jin, D. Wang. A Computational Auditory Scene Analysis System for Robust Speech Recognition. Computer Speech & Language. Vol. 24, No. 1, pp. 77-93, 2010.   DOI   ScienceOn
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8 S. Y. Cho, D. M. Sun, Z. D. Qiu. A Spearman correlation coefficient ranking for matching-score fusion on speaker recognition. Proc. TENCON Conf. pp. 736-741, 2011.
9 Sang-Yeob Oh. Improving Phoneme Recognition based on Gaussian Model using Bhattacharyya Distance Measurement Method. Journal of Korea Multimedia Society. Vol. 14, No. 1, pp. 85-93, 2011.   과학기술학회마을   DOI   ScienceOn
10 Jong-Young Ahn, Sang-Bum Kim, Su-Hoon Kim, Kang-In Hur. A study on Voice Recognition using Model Adaptation HMM for Mobile Environment. The Journal of the Institute of Webcasting, Internet and Telecommunication. Vol. 11, No. 3, pp. 175-179, 2011.   과학기술학회마을
11 Sang-Yeob Oh. Selective Speech Feature Extraction using Channel Similarity in CHMM Vocabulary Recognition. The Journal of digital policy and management. Vol. 11, No. 10, pp. 453-458, 2013.   과학기술학회마을   DOI