A Study on the Recognition of Korean Numerals Using Recurrent Neural Predictive HMM

회귀신경망 예측 HMM을 이용한 숫자음 인식에 관한 연구

  • 김수훈 (부천대학 정보통신계열) ;
  • 고시영 (경일대학교 전자정보공학과) ;
  • 허강인 (동아대학교 전자공학과)
  • Published : 2001.11.01


In this paper, we propose the Recurrent Neural Predictive HMM (RNPHMM). The RNPHMM is the hybrid network of the recurrent neural network and HMM. The predictive recurrent neural network trained to predict the future vector based on several last feature vectors, and defined every state of HMM. This method uses the prediction value from the predictive recurrent neural network, which is dynamically changing due to the effects of the previous feature vectors instead of the stable average vectors. The models of the RNPHMM are Elman network prediction HMM and Jordan network prediction HMM. In the experiment, we compared the recognition abilities of the RNPHMM as we increased the state number, prediction order, and number of hidden nodes for the isolated digits. As a result of the experiments, Elman network prediction HMM and Jordan network prediction HMM have good recognition ability as 98.5% for test data, respectively.


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