NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic detection of poor speech recognition at the dialogue level
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance
User Modeling and User-Adapted Interaction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Leveraging data about users in general in the learning of individual user models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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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.