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

Abstract

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. Key Words : CHMM(Continuous Hidden Markov Model), GMM(Gaussian Mixture Model), k-means, vocabulary search, vocabulary recognition Received 6 November 2014, Revised 16 December 2014Accepted 20 February 2015Corresponding Author: Lee Jong Sub(The University of Semyung)Email: 99jslee@semyung.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.