Abstract
Abstract In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate has the disadvantage that require sophisticated smoothing process. Gaussian mixtures in order to improve them with a continuous probability density CHMM (Continuous Hidden Markov Model) model is proposed for the optimization of the library system. In this paper is system configuration thread control in recognition Gaussian mixtures model provides a model to optimize of the CHMM vocabulary recognition. The result of applying the proposed system, the recognition rate of 98.1% in vocabulary recognition, respectively. Key Words : Gaussian model, Model optimization, CHMM, Vocabulary Recognition, Library system ※본 논문은 2012년 가천대학교의 교내연구비 지원에 의한 결과임. (GCU-2012-R147) * 광운대학교 컴퓨터공학과 박사과정 ** 가천대학교 IT대학 인터랙티브미디어학과 교수(교신저자)논문접수: 2012년 7월 19일, 1차 수정을 거쳐, 심사완료: 2012년 8월 20일
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