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Volumn , Issue , 2006, Pages 185-192

Using maximum entropy (ME) model to incorporate gesture cues for SU detection

Author keywords

Gesture; Language models; Meetings; Multimodal fusion; Prosody; Sentence boundary detection

Indexed keywords

ARTIFICIAL INTELLIGENCE; HIDDEN MARKOV MODELS; IMAGE RECOGNITION; LEARNING SYSTEMS; SPEECH RECOGNITION;

EID: 34547201931     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1180995.1181035     Document Type: Conference Paper
Times cited : (5)

References (24)
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    • The role of gesture in communication and thinking
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    • Goldin-Meadow, S.1
  • 13
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    • Resampling techniques for Sentence Boundary Detection: A Case Study in Machine Learning from Imbalanced Data for Spoken Language Processing
    • to appear
    • Y. Liu, N. V. Chawla, E. Shriberg, A. Stolcke, and M. Harper. Resampling techniques for Sentence Boundary Detection: A Case Study in Machine Learning from Imbalanced Data for Spoken Language Processing. Computer Speech and Language, to appear.
    • Computer Speech and Language
    • Liu, Y.1    Chawla, N.V.2    Shriberg, E.3    Stolcke, A.4    Harper, M.5
  • 14
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    • Comparing HMM, Maximum Entropy, and Conditional Random Fields for disfluency detection
    • Lisbon, Sept
    • Y. Liu, E. Shriberg, A. Stockle, and M. Harper. Comparing HMM, Maximum Entropy, and Conditional Random Fields for disfluency detection. In Proc. of InterSpeech, Lisbon, Sept. 2005.
    • (2005) Proc. of InterSpeech
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    • Rabiner, L.R.1    Juang, B.H.2
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    • Prosody-based automatic segmentation of speech into sentences and topics
    • E. Shriberg, A. Stolcke, D. Hakkani-Tur, and G. Tur. Prosody-based automatic segmentation of speech into sentences and topics. Speech Communication, 32(1-2):127-154, 2000.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.