Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Vocabulary Recognition Performance Improvement using a convergence of Bayesian Method for Parameter Estimation and Bhattacharyya Algorithm Model

모수 추정을 위한 베이시안 기법과 바타차랴 알고리즘을 융합한 어휘 인식 성능 향상

  • Oh, Sang-Yeob (Dept. of Computer Engineering, Gachon University)
  • 오상엽 (가천대학교 컴퓨터공학과)
  • Received : 2015.08.09
  • Accepted : 2015.10.20
  • Published : 2015.10.28
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Abstract

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

어휘 인식 시스템은 학습 모델을 구성하여 인식하므로 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타난다. 이런 경우 인식 모델을 확장할 수 있도록 재구성하거나 인식 모델 구성 시 확장성을 반영하므로 해결할 수 있다. 본 논문에서는 모델 구성 시 확장성을 반영할 수 있는 모수 추정을 위한 베이시안 기법을 사용하여 바타차랴 알고리즘 음성 인식 학습 모델 구성 방법을 융합하여 제안하였다. 음소가 갖는 특징을 기반으로 학습 데이터의 음소에 모수 추정을 위한 베이시안 기법을 이용하였고 유사한 학습 모델은 바타챠랴 알고리즘을 이용하여 정확한 학습 모델로 인식하도록 하였다. 바타챠랴 알고리즘 인식 모델을 구성하여 인식 성능을 평가하였다. 본 논문에서 제안한 시스템을 적용한 결과 어휘 인식률에서 97.5%의 인식률과 1.2초의 학습 시간을 나타내었다.

Keywords

References

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