Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization

HMM 어휘 인식 모델 최적화를 이용한 베이시안 기법 인식률 향상

  • Oh, Sang Yeon (Dept. of Computer Media Convergence, Gachon University)
  • 오상엽 (가천대학교 컴퓨터미디어융합학과)
  • Received : 2014.05.10
  • Accepted : 2014.07.20
  • Published : 2014.07.28
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Abstract

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

HMM(Hidden Markov Model)을 이용한 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 HMM(Hidden Markov Model) 모델 최적화를 이용한 베이시안 기법 인식률 향상을 제안한다. 본 논문은 HMM 어휘 인식에서 인식을 위한 모델 구성을 가우시안 믹스쳐 모델로 최적화한 인식 모델을 생성하였으며 베이시안 기법인 사전확률과 사후확률을 이용한 인식률을 향상시켰다. 본 논문에서 제안한 방법을 적용한 결과 어휘인식률에서 97.9%의 인식률을 나타내었다.

Keywords

References

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