Journal of Computing Science and Engineering

  • 제2권1호
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  • Pages.1-25
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  • 2008
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Using Utterance and Semantic Level Confidence for Interactive Spoken Dialog Clarification

  • Jung, Sang-Keun (Computer Science and Engineering Pohang University of Science and Technology) ;
  • Lee, Cheong-Jae (Computer Science and Engineering Pohang University of Science and Technology) ;
  • Lee, Gary Geunbae (Computer Science and Engineering Pohang University of Science and Technology)
  • 발행 : 2008.03.31
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초록

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between the user's intention and the system's understanding, which eventually results in a misinterpretation. To fill in the gap, people in human-to-human dialogs try to clarify the major causes of the misunderstanding to selectively correct them. This paper presents a method of clarification techniques to human-to-machine spoken dialog systems. We viewed the clarification dialog as a two-step problem-Belief confirmation and Clarification strategy establishment. To confirm the belief, we organized the clarification process into three systematic phases. In the belief confirmation phase, we consider the overall dialog system's processes including speech recognition, language understanding and semantic slot and value pairs for clarification dialog management. A clarification expert is developed for establishing clarification dialog strategy. In addition, we proposed a new design of plugging clarification dialog module in a given expert based dialog system. The experiment results demonstrate that the error verifiers effectively catch the word and utterance-level semantic errors and the clarification experts actually increase the dialog success rate and the dialog efficiency.

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참고문헌

  1. COX, S. AND DASMAHAPATRA, S. 2000. A semantically-based confidence measure for speech recognition. Proc. of ICSLP 4, 206−209.
  2. EUN, J., LEE, C., AND LEE, G. 2004. An information extraction approach for spoken language understanding. Proc. of ICSLP, 2145−2148.
  3. GABSDIL, M. AND BOS, J. 2003. Combining acoustic confidence scores with deep semantic analysis for clarification dialogues. Proc. of the 5th international workshop on computational semantics (IWCS-5), 137−150.
  4. HAZEN, T., BURIANEK, T., POLIFRONI, J., AND SENEFF, S. 2000. Recognition Confidence Scoring for Use in Speech Understanding Systems. Proc. of the the ISCA ASR.
  5. HAZEN, T., SENEFF, S., AND POLIFRONI, J. 2002. Recognition confidence scoring and its use in speech understanding systems. Computer Speech and Language 16, 1, 49−67.
  6. HEISTERKAMP, P. AND MCGLASHAN, S. 1996. Units of dialogue management: an example. Proc. of ICSLP 1, 200-203.
  7. HIGASHINAKA, R., SUDOH, K., AND NAKANO, M. 2005. Incorporating Discourse Features into Confidence Scoring of Intention Recognition Results in Spoken Dialogue Systems. Proc. of ICASSP 1, 25-28.
  8. HUET, S., GRAVIER, G., AND SEBILLOT, P. 2007. Morphosyntatic processing of N-best lists for improved recognition and confidence measure computation. Proc. of the 8th Eurospeech, 1741-1744.
  9. JIANG, H. 2005. Confidence measures for speech recognition: A survey. Speech Communication 45, 4, 455-470. https://doi.org/10.1016/j.specom.2004.12.004
  10. KAMPPARI, S. AND HAZEN, T. 2000. Word and phone level acoustic confidence scoring. Proc. of ICASSP 3, 1799-1802.
  11. LEE, C., JUNG, S., EUN, J., JEONG, M., AND LEE, G. 2006. A situation-based dialog management using dialog examples. In Proc. of the ICASSP. 69-72.
  12. MCTEAR, M., O'NEILL, I., HANNA, P., AND LIU, X. 2005. Handling errors and determining confirmation strategies: An object-based approach. Speech communication 45, 3, 249-269. https://doi.org/10.1016/j.specom.2004.11.006
  13. MISU, T. AND KAWAHARA, T. 2006. Dialogue strategy to clarify users queries for document retrieval system with speech interface. Speech Communication 48, 9, 1137-1150. https://doi.org/10.1016/j.specom.2006.04.001
  14. ONEILL, I., HANNA, P., LIU, X., GREER, D., AND MCTEAR, M. 2005. Implementing advanced spoken dialogue management in Java. Science of Computer Programming 54, 1, 99-124. https://doi.org/10.1016/j.scico.2004.05.006
  15. PAEK, T. AND HORVITZ, E. 2000. Conversation as action under uncertainty. Proc. of Uncertainty in Artificial Intelligence, 455−464.
  16. PURVER, M. 2006. CLARIE: Handling Clarification Requests in a Dialogue System. Research on Language & Computation 4, 2, 259−288.
  17. RIESER, V. AND LEMON, O. 2006. Using machine learning to explore human multimodal clarification strategies. Proc. of the COLING/ACL on Main conference poster sessions, 659−666.
  18. RUDNICKY, A., THAYER, E., CONSTANTINIDES, P., TCHOU, C., SHERN, R., LENZO, K., XU, W., AND OH, A. 1999. Creating natural dialogs in the Carnegie Mellon Communicator system. Proc. of Eurospeech 4, 1531-1534.

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