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http://dx.doi.org/10.15207/JKCS.2017.8.5.013

Voice Recognition Performance Improvement using the Convergence of Voice signal Feature and Silence Feature Normalization in Cepstrum Feature Distribution  

Hwang, Jae-Cheon (Department of Computer Engineering, Gachon University)
Publication Information
Journal of the Korea Convergence Society / v.8, no.5, 2017 , pp. 13-17 More about this Journal
Abstract
Existing Speech feature extracting method in speech Signal, there are incorrect recognition rates due to incorrect speech which is not clear threshold value. In this article, the modeling method for improving speech recognition performance that combines the feature extraction for speech and silence characteristics normalized to the non-speech. The proposed method is minimized the noise affect, and speech recognition model are convergence of speech signal feature extraction to each speech frame and the silence feature normalization. Also, this method create the original speech signal with energy spectrum similar to entropy, therefore speech noise effects are to receive less of the noise. the performance values are improved in signal to noise ration by the silence feature normalization. We fixed speech and non speech classification standard value in cepstrum For th Performance analysis of the method presented in this paper is showed by comparing the results with CHMM HMM, the recognition rate was improved 2.7%p in the speech dependent and advanced 0.7%p in the speech independent.
Keywords
Voice recognition; feature extract; silence feature normalization; voice feature; noise;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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