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

베이시안 기법과 선택적 음성특징 추출을 융합한 음성 인식 성능 향상

  • 황재천 (가천대학교 컴퓨터공학과)
  • Received : 2016.11.01
  • Accepted : 2016.12.20
  • Published : 2016.12.31

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

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