지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2006.7
- 수록면
- 410 - 413 (4page)
이용수
초록· 키워드
오류제보하기
Errors on continuous speech recognition are decreasing according to the development of speech recognition technology. However, it is still not enough to apply toward real dialog models. For the purpose of information retrieval, the error correction using semantic analysis of neighborhood words is still difficult. And the previous methods without considering Korean's euphonic changes don't reflect exactly the features of Korean phoneme.
This paper proposes a keyword error correction using triphone error probability model that considers euphonic changes in the dialog model for information retrieval. Some experiments in a weather information retrieval domain were carried out on the HTK which is a common speech recognition program. The continuous speech recognition training data were used by combining existing variety of different DBs. The experimental results showed that the triphone error probability model has 98.3 percent of error correction.
This paper proposes a keyword error correction using triphone error probability model that considers euphonic changes in the dialog model for information retrieval. Some experiments in a weather information retrieval domain were carried out on the HTK which is a common speech recognition program. The continuous speech recognition training data were used by combining existing variety of different DBs. The experimental results showed that the triphone error probability model has 98.3 percent of error correction.
목차
ABSTRACT
1. INTRODUCTION
2. PREVIOUS STUDY
3. KEYWORD ERROR CORRECTION
4. EXPERIMENTAL RESULTS
5. CONCLUSION
6. REFERENCES
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UCI(KEPA) : I410-ECN-0101-2012-004-004247355