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http://dx.doi.org/10.15207/JKCS.2016.7.6.007

Voice Recognition Performance Improvement using the Convergence of Bayesian method and Selective Speech Feature  

Hwang, Jae-Chun (Division of Computer Engineering, Gachon University)
Publication Information
Journal of the Korea Convergence Society / v.7, no.6, 2016 , pp. 7-11 More about this Journal
Abstract
Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods in vocabulary recognition, respectively.
Keywords
Voice recognition; Bayesian method; voice extract; filter bank; voice feature; convergence method;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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