디지털융복합연구 (Journal of Digital Convergence)

  • 제13권8호
  • /
  • Pages.295-300
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  • 2015
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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HMM을 기반으로 한 사전 확률의 문제점을 해결하기 위해 베이시안 기법 어휘 인식 모델에의 사후 확률을 융합한 잡음 제거

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

  • 오상엽 (가천대학교 컴퓨터공학과)
  • Oh, Sang-Yeob (Dept. of Computer Media Convergence, Gachon University)
  • 투고 : 2015.06.20
  • 심사 : 2015.08.20
  • 발행 : 2015.08.28
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⟨ 이전 논문 다음 논문 ⟩

초록

사전 확률분포를 모델링하는 HMM을 사용하는 어휘 인식에서 인식 어휘의 모델들의 대한 인식 확률이 이산적인 분포를 나타내며 인식을 위한 계산량이 적은 장점이 있지만 인식률을 계산했을 때 상대적으로 낮은 단점이 있다. 이를 개선하기 위하여 베이시안 기법 어휘 인식 모델을 융합한 잡음 제거 인식률 향상을 제안한다. 본 논문은 베이시안 기법 어휘 인식을 위한 모델 구성을 베이시안 기법의 최적화한 인식 모델을 구성하였다. HMM을 기반으로 한 사전 확률 방법과 베이시안 기법인 사후확률을 융합하여 잡음을 제거하고 인식률을 향상시켰다. 본 논문에서 제안한 방법을 적용한 결과 어휘 인식률에서 98.1%의 인식률을 나타내었다.

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.

키워드

참고문헌

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