Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Vocabulary Recognition Performance Improvement using k-means Algorithm for GMM Support

GMM 지원을 위해 k-means 알고리즘을 이용한 어휘 인식 성능 개선

  • Received : 2014.11.06
  • Accepted : 2015.02.20
  • Published : 2015.02.28
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Abstract

General CHMM vocabulary recognition system is model observation probability for vocabulary recognition of recognition rate's low. Used as the limiting unit is applied only to some problem in the phoneme model. Also, they have a problem that does not conform to the needs of the search range to meaning of the words in the vocabulary. Performs a phoneme recognition using GMM to improve these problems. We solve the problem according to the limited search words characterized by an improved k-means algorithm. Measure the effectiveness represented by the accuracy and reproducibility as compared to conventional system performance experiments. Performance test results accuracy is 83%p, and recall is 67%p.

일반적인 CHMM 어휘 인식 시스템은 어휘 인식에 대한 모델들의 관측 확률 인식률이 낮고, 일부 단위 음소 모델에만 적용되어 제한적으로 사용되는 문제점이 있다. 또한, 어휘 탐색에서 어휘의 의미가 다양하여 탐색된 어휘가 사용자의 요구에 부합되지 않는 문제점을 가진다. 이러한 문제를 개선하기 위해 GMM(Gaussian Mixture Model)을 이용한 음소인식을 수행하고, 개선된 k-means 알고리즘을 이용하여 어휘 특성에 따른 제한적인 탐색 문제점을 해결하였다. 성능 실험은 기존의 시스템과 비교하여 정확도와 재현율로 대변되는 효과성을 측정하였으며, 성능 실험 결과 정확도는 83%, 재현율은 67%로 나타났다.

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