DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoo, Chang-Dong | - |
dc.contributor.advisor | 유창동 | - |
dc.contributor.author | Jin, Min-Ho | - |
dc.contributor.author | 진민호 | - |
dc.date.accessioned | 2011-12-14T01:53:51Z | - |
dc.date.available | 2011-12-14T01:53:51Z | - |
dc.date.issued | 2004 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=238478&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/37801 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학전공, 2004, [ vi, 37 p. ] | - |
dc.description.abstract | Automatic Speech Recognition (ASR) has made great advances in last 10 years. The application of ASR in everyday situation represents a new possibility in human-machine interface. For this to become a reality, it is important to distinguish in-vocabulary words from out-of-vocabulary words. In order to reject out-of-vocabulary words effectively while accepting in-vocabulary words, conventional approaches such as the filler model approach or the on-line garbage model approach have been proposed. These approaches either require the use of extraneous data to train filler models or require adjusting when the set of in-vocabulary words is changed. In this thesis, a novel approach based on probabilistic characteristics is proposed to reduce the confusion between a claimed model and the other models. In order to reflect probabilistic characteristics of models, the anti-model for the claimed model is constructed by weighting observation probabilities of other models with their weights being inversely proportional to their distances to the claimed model. In addition, a hybrid of the proposed model and the on-line garbage model is also suggested to improve performance. The proposed method is evaluated using 455 Korean isolated words speech corpus. For simulation, 90 words are selected as in-vocabulary words and the same number of words are selected as out-of-vocabulary words without overlapping. The proposed method results in 8.33% of equal error rate, which is improved by 63.16% for the filler model approach and 39.77% for the on-line garbage model in error rate reduction. | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | OUT-OF-VOCABULARY REJECTION | - |
dc.subject | HIDDEN MARKOV MODEL | - |
dc.subject | 은닉 마르코프 모델 | - |
dc.subject | 비인식 대상 어휘 제거 | - |
dc.title | (An) out-of-vocabulary rejection technique using anti-phoneme model and on-line garbage model in hidden Markov model | - |
dc.title.alternative | 은닉 마르코프 모델에서의 반음소 모델과 온라인 가비지 모델을 이용한 비인식대상 어휘 제거 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 238478/325007 | - |
dc.description.department | 한국과학기술원 : 전기및전자공학전공, | - |
dc.identifier.uid | 020023603 | - |
dc.contributor.localauthor | Yoo, Chang-Dong | - |
dc.contributor.localauthor | 유창동 | - |
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