11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Improving Cross Database Prediction of Dialogue Quality Using Mixture of Experts

Klaus-Peter Engelbrecht, Hamed Ketabdar, Sebastian Möller

Deutsche Telekom Laboratories, Germany

Models for the prediction of user judgments from interaction data can be used in different contexts such as system quality assessment, monitoring of deployed systems, or as reward function in learned dialog managers. Such models still show a considerable lack with respect to their generalizability. This paper specifically addresses this issue. We propose to use a Mixture of Experts approach for cross database predictions. In Mixture of Experts, several classifiers are trained on subsets of the data showing specific characteristics. Predictions of each expert model are combined for the overall prediction result. We show that such an approach can improve the cross database prediction accuracy.

Full Paper

Bibliographic reference.  Engelbrecht, Klaus-Peter / Ketabdar, Hamed / Möller, Sebastian (2010): "Improving cross database prediction of dialogue quality using mixture of experts", In INTERSPEECH-2010, 1337-1340.