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Noise Removal using a Convergence of the posteriori probability of the Bayesian techniques vocabulary recognition model to solve the problems of the prior probability based on HMM

HMM을 기반으로 한 사전 확률의 문제점을 해결하기 위해 베이시안 기법 어휘 인식 모델에의 사후 확률을 융합한 잡음 제거

  • Oh, Sang-Yeob (Dept. of Computer Media Convergence, Gachon University)
  • 오상엽 (가천대학교 컴퓨터공학과)
  • Received : 2015.06.20
  • Accepted : 2015.08.20
  • Published : 2015.08.28

Abstract

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

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