디지털융복합연구 (Journal of Digital Convergence)

  • 제13권11호
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  • Pages.165-170
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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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벡터모델 기반 바타챠랴 거리 측정 기법과 우도 원리 베이시안을 융합한 어휘 인식 모델

Vocabulary Recognition Model using a convergence of Likelihood Principla Bayesian methode and Bhattacharyya Distance Measurement based on Vector Model

  • 오상엽 (가천대학교 컴퓨터미디어융합학과)
  • Oh, Sang-Yeob (Dept. of Computer Engineering, Gachon University)
  • 투고 : 2015.09.24
  • 심사 : 2015.11.20
  • 발행 : 2015.11.28
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초록

어휘 인식 시스템은 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타나며, 기존의 시스템은 벡터 값을 모델로 만들어 데이터베이스로 구성하여 어휘 인식에 사용하였다. 어휘 인식을 위한 탐색 중에 형성되는 모델은 데이터베이스로 구성되어 있지 않아 인식할 수 없는 단점이 존재한다. 따라서 본 논문에서는 특징 벡터 모델을 기반으로 바타챠랴 거리 측정법을 이용한 베이시안 인식 모델을 구성하여 탐색 중에 형성되는 벡터 모델을 인식할 수 있도록 유도하였으며, 위너 필터를 적용하여 인식률을 향상시켰다. 2 방법을 융합하여 실험한 결과 향상된 신뢰도로 인해 높은 인식 성능을 확인하였으며, 본 논문에서 제안한 측정법을 이용하여 기존의 방법들에 비하여 평균 98.2%의 성능을 나타내었다.

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. The vector values of the existing system to the model created by configuring the database was used in the recognition vocabulary. The model to be formed during the search for the recognition vocabulary is recognizable because there is a disadvantage not configured with a database. In this paper, it induced to recognize the vector model is formed by the search and configuration using a Bayesian model recognizes the Bhattacharyya distance measurement based on the vector model, by applying the Wiener filter improves the recognition rate. The result of Convergence of two method's are improved reliability experiments for distance measurement. Using a proposed measurement are compared to the conventional method exhibited a performance of 98.2%.

키워드

참고문헌

  1. Jong-Sub Lee, Sang-Yeob Oh. Vocabulary Retrieve System using Improve Levenshtein Distance algorithm. The Journal of digital policy & management v.11 no.11, pp.367-372, 2013.
  2. Sang-Yeob Oh. Improving Phoneme Recognition based on Gaussian Model using Bhattacharyya Distance Measurement Method. Journal of Korea Multimedia Society. v.14 no.1, pp.85-93, 2011. https://doi.org/10.9717/kmms.2011.14.1.085
  3. Sang-Yeob Oh. Speech Recognition Optimization Learning Model using HMM Feature Extraction In the Bhattacharyya Algorithm. The Journal of digital policy & management v.11 no.6, pp.199-204, 2013.
  4. SangYeob Oh. Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization. Journal of digital convergence v.12 no.7, pp.273-278, 2014. https://doi.org/10.14400/JDC.2014.12.7.273
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  11. Chan-Shik Ahn, Sang-Yeob Oh. Efficient Continuous Vocabulary Clustering Modeling for Tying Model Recognition Performance Improvement. Journal of the Korea Society of Computer and Information. v.15, no.1, pp.177-183, 2010. https://doi.org/10.9708/jksci.2010.15.1.177
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