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


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;Vocabulary Recognition;Model Optimize;Bayesian;Recognition Rate


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