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성과물

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구분 SCI
연도 ~ 키워드

상세정보

학술지 레벤스타인 거리에 기초한 위치 정확도를 이용한 고립 단어 인식 결과의 비유사 후보 단어 제외
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저자
윤영선, 강점자
발행일
200909
출처
말소리와 음성과학, v.1 no.3, pp.109-115
ISSN
2005-8063
출판사
한국음성학회
협약과제
09MS3900, 신성장동력산업용 대용량 대화형 분산 처리 음성인터페이스 기술개발, 이윤근
초록
Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. In this paper, we investigate several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. At first, word distance method based on phone and syllable distances are considered. These methods use just Levenstein distance on phones or double Levenstein distance algorithm on syllables of candidates. Next, word similarity approaches are presented that they use characters' position information of word candidates. Each character‘s position is labeled to inserted, deleted, and correct position after alignment between source and target string. The word similarities are obtained from characters' positional probabilities which mean the frequency ratio of the same characters' observations on the position. From experimental results, we can find that the proposed methods are effective for removing non-similar words without loss of system performance from the N-best recognition candidates of the systems.
KSP 제안 키워드
Correct position, Distance method, Isolated Word Recognition, Non-similar, Position information, Similar words, System performance, Word similarity, distance algorithm, distance measure, frequency ratio