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

User behaviors on a system vary not only among individuals Brit 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-terra 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 air 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.