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Volumn , Issue , 2013, Pages 58-62

Applying Unsupervised Learning to Support Vector Space Model Based Speaking Assessment

Author keywords

Document clustering; Speech assessment; Unsupervised learning; Vector Space Model (VSM)

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS; VECTOR SPACES; VECTORS;

EID: 85121756293     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1)

References (14)
  • 5
    • 84863369686 scopus 로고    scopus 로고
    • Improved pronunciation features for construct-driven assessment of non-native spontaneous speech
    • L. Chen, K. Zechner, and X Xi. 2009. Improved pronunciation features for construct-driven assessment of non-native spontaneous speech. In NAACL-HLT.
    • (2009) NAACL-HLT
    • Chen, L.1    Zechner, K.2    Xi, X3
  • 7
    • 77955350104 scopus 로고    scopus 로고
    • EduSpeak: a speech recognition and pronunciation scoring toolkit for computer-aided language learning applications
    • H. Franco, H. Bratt, R. Rossier, V. Rao Gadde, E. Shriberg, V. Abrash, and K. Precoda. 2010. EduSpeak: a speech recognition and pronunciation scoring toolkit for computer-aided language learning applications. Language Testing, 27(3):401.
    • (2010) Language Testing , vol.27 , Issue.3 , pp. 401
    • Franco, H.1    Bratt, H.2    Rossier, R.3    Rao Gadde, V.4    Shriberg, E.5    Abrash, V.6    Precoda, K.7
  • 8
    • 33646865685 scopus 로고    scopus 로고
    • Identifying off-topic student essays without topic-specific training data
    • D. Higgins, J. Burstein, and Y. Attali. 2006. Identifying off-topic student essays without topic-specific training data. Natural Language Engineering, 12.
    • (2006) Natural Language Engineering , vol.12
    • Higgins, D.1    Burstein, J.2    Attali, Y.3
  • 9
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • Thomas Hofmann. 2001. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1):177–196.
    • (2001) Machine Learning , vol.42 , Issue.1 , pp. 177-196
    • Hofmann, Thomas1
  • 13
    • 84926181768 scopus 로고    scopus 로고
    • Exploring content features for automated speech scoring
    • Montreal, July
    • S. Xie, K. Evanini, and K. Zechner. 2012. Exploring content features for automated speech scoring. Proceedings of the NAACL-HLT, Montreal, July.
    • (2012) Proceedings of the NAACL-HLT
    • Xie, S.1    Evanini, K.2    Zechner, K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.