Advertisement

Bus Information System Based on User Models and Dynamic Generation of VoiceXML Scripts

  • Shinichi Ueno
  • Fumihiro Adachi
  • Kazunori Komatani
  • Tatsuya Kawahara
  • Hiroshi G. Okuno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3609)

Abstract

We have developed a telephone-based cooperative natural language dialogue system. Since natural language involves very various expressions, a large number of VoiceXML scripts need to be prepared to handle all possible input patterns. Thus, flexible dialogue management for various user utterances is realized by generating VoiceXML scripts dynamically. Moreover, we address the issue of appropriate user modeling to generate cooperative responses to users. Specifically, three dimensions of user models are set up: the skill level to the system, the knowledge level on the target domain and the degree of hastiness. The models are automatically derived by decision tree learning using real dialogue data collected by the system. Experimental evaluation showed that the cooperative responses adapted to individual users served as good guides for novices without increasing the duration of dialogue for skilled users.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Komatani, K., Kawahara, T.: Flexible mixed-initiative dialogue management using concept-level confidence measures of speech recognizer output. In: Proc. Int’l Conf. Computational Linguistics (COLING), pp. 467–473 (2000)Google Scholar
  2. 2.
    Hazen, T.J., Burianek, T., Polifroni, J., Seneff, S.: Integrating recognition confidence scoring with language understanding and dialogue modeling. In: Proc. Int’l Conf. Spoken Language Processing (ICSLP) (2000)Google Scholar
  3. 3.
    Litman, D.J., Pan, S.: Predicting and adapting to poor speech recognition in a spoken dialogue system. In: Proc. of the 17th National Conference on Artificial Intelligence (AAAI2000) (2000)Google Scholar
  4. 4.
    Chu-Carroll, J.: MIMIC: An adaptive mixed initiative spoken dialogue system for information queries. In: Proc. of the 6th Conf. on Applied Natural Language Processing, pp. 97–104 (2000)Google Scholar
  5. 5.
    Lamel, L., Rosset, S., Gauvain, J.L., Bennacef, S.: The LIMSI ARISE system for train travel information. In: IEEE Int’l Conf. Acoust., Speech & Signal Processing (ICASSP), IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  6. 6.
    Sadek, D.: Design considerations on dialogue systems: From theory to technology -the case of artimis-. In: Proc. ESCA workshop on Interactive Dialogue in Multi-Modal Systems (1999)Google Scholar
  7. 7.
    Kass, R., Finin, T.: Modeling the user in natural language systems. Computational Linguistics 14, 5–22 (1988)Google Scholar
  8. 8.
    van Beek, P.: A model for generating better explanations. In: Proc. of the 25th Annual Meeting of the Association for Computational Linguistics (ACL-87), pp. 215–220 (1987)Google Scholar
  9. 9.
    Paris, C.L.: Tailoring object descriptions to a user’s level of expertise. Computational Linguistics 14, 64–78 (1988)Google Scholar
  10. 10.
    Elzer, S., Chu-Carroll, J., Carberry, S.: Recognizing and utilizing user preferences in collaborative consultation dialogues. In: Proc. of the 4th Int’l Conf. on User Modeling, pp. 19–24 (1994)Google Scholar
  11. 11.
    Eckert, W., Levin, E., Pieraccini, R.: User modeling for spoken dialogue system evaluation. In: Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 80–87. IEEE Computer Society Press, Los Alamitos (1997)CrossRefGoogle Scholar
  12. 12.
    Pargellis, A., Kuo, J., Lee, C.H.: Automatic dialogue generator creates user defined applications. In: Proc. European Conf. Speech Commun. & Tech (EUROSPEECH) (1999)Google Scholar
  13. 13.
    Nyberg, E., Mitamura, T., Placeway, P., Duggan, M., Hataoka, N.: Dialogxml: Extending voicexml for dynamic dialog management. In: Proc. of Human Language Technology 2002 (HLT2002), pp. 286–291 (2002)Google Scholar
  14. 14.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993), http://www.rulequest.com/see5-info.html Google Scholar
  15. 15.
    Over, P.: Trec-7 interactive track report. In: Proc. of the 7th Text REtrieval Conference (TREC7) (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Shinichi Ueno
    • 1
  • Fumihiro Adachi
    • 1
  • Kazunori Komatani
    • 1
  • Tatsuya Kawahara
    • 1
  • Hiroshi G. Okuno
    • 1
  1. 1.Kyoto University, Kyoto 606-8501Japan

Personalised recommendations