A Model of Temporally Changing User Behaviors in a Deployed Spoken Dialogue System

  • Kazunori Komatani
  • Tatsuya Kawahara
  • Hiroshi G. Okuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)


User behaviors on a system vary not only among individuals but also within the same user when he/she gains experience on the system. We empirically investigated how individual users changed their behaviors on the basis of long-term data, which were collected by our telephone-based spoken dialogue system deployed for the open public over 34 months. The system was repeatedly used by citizens, who were each identified by their phone numbers. We conducted an experiment by using these data and showed that prediction accuracy of utterance-understanding errors improved when the temporal change was taken into consideration. This result showed that modeling temporally changing user behaviors was helpful in improving the performance of spoken dialogue systems.


Spoken dialogue system temporal change real user behavior habituation barge-in deployed system 


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  1. 1.
    Komatani, K., Ueno, S., Kawahara, T., Okuno, H.G.: User modeling in spoken dialogue systems to generate flexible guidance. User Modeling and User-Adapted Interaction 15(1), 169–183 (2005)CrossRefGoogle Scholar
  2. 2.
    Walker, M., Langkilde, I., Wright, J., Gorin, A., Litman, D.: Learning to predict problematic situations in a spoken dialogue system: Experiments with how may I help you? In: Proc. NAACL, pp. 210–217 (2000)Google Scholar
  3. 3.
    Jameson, A., Wittig, F.: Leveraging data about users in general in the learning of individual user models. In: Proc. IJCAI 2001 (2001)Google Scholar
  4. 4.
    Litman, D.J., Walker, M.A., Kearns, M.S.: Automatic detection of poor speech recognition at the dialogue level. In: Proc. ACL, pp. 309–316 (1999)Google Scholar
  5. 5.
    Gabsdil, M., Lemon, O.: Combining acoustic and pragmatic features to predict recognition performance in spoken dialogue systems. In: Proc. ACL, pp. 343–350 (2004)Google Scholar
  6. 6.
    Bohus, D., Rudnicky, A.: A “k hypotheses + other” belief updating model. In: Proc. AAAI Workshop on Statistical and Empirical Approaches to Spoken Dialogue Systems (2006)Google Scholar
  7. 7.
    Ström, N., Seneff, S.: Intelligent barge-in in conversational systems. In: Proc. ICSLP, pp. 652–655 (2000)Google Scholar
  8. 8.
    Rose, R., Kim, H.: A hybrid barge-in procedure for more reliable turn-taking in human-machine dialog systems. In: Proc. of ASRU, pp. 198–203 (2003)Google Scholar
  9. 9.
    Komatani, K., Kawahara, T., Okuno, H.G.: Analyzing temporal transition of real user’s behaviors in a spoken dialogue system. In: Proc. INTERSPEECH, pp. 142–145 (2007)Google Scholar
  10. 10.
    Raux, A., Bohus, D., Langner, B., Black, A., Eskenazi, M.: Doing research on a deployed spoken dialogue system: One year of Let’s Go! experience. In: Proc. INTERSPEECH, pp. 65–68 (2006)Google Scholar
  11. 11.
    Komatani, K., Kawahara, T., Okuno, H.G.: Predicting asr errors by exploiting barge-in rate of individual users for spoken dialogue systems. In: Proc. INTERSPEECH, pp. 183–186 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kazunori Komatani
    • 1
  • Tatsuya Kawahara
    • 1
  • Hiroshi G. Okuno
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

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