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http://dx.doi.org/10.14400/JDC.2015.13.10.353

Vocabulary Recognition Performance Improvement using a convergence of Bayesian Method for Parameter Estimation and Bhattacharyya Algorithm Model  

Oh, Sang-Yeob (Dept. of Computer Engineering, Gachon University)
Publication Information
Journal of Digital Convergence / v.13, no.10, 2015 , pp. 353-358 More about this Journal
Abstract
The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.
Keywords
Bayesian Method; Parameter Estimation; Bhattacharyya Algorithm; Recognition Model; Vocabulary Recognition;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
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1 SangYeob Oh. Decision Tree State Tying Modeling Using Parameter Estimation of Bayesian Method. Journal of digital convergence v.13 no.1, pp.243-248, 2015.   DOI
2 SangYeob Oh. Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization. Journal of digital convergence v.12 no.7, pp.273-278, 2014.   DOI
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4 Sang-Yeob Oh. Decision Tree for Likely phoneme model schema support. The Journal of digital policy & management v.11 no.10, pp.367-372, 2013.
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13 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. v.15, no.1, pp.177-183, 2010.   DOI   ScienceOn
14 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.
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