AKYOL, A: Increasing Robustness of Spoken Dialogue Systems

In conversational dialogue applications it is critical to understand the requests accurately. However, the performance of current speech recognition systems are far from perfect. In order to function effectively with imperfect speech recognition, an accurate confidence scoring mechanism should be employed. To determine a confidence score for a hypothesis, certain confidence features are combined. In this work, the performance of filler-model based confidence features are investigated. Five types of filler model are defined: triphone-network, phone-network, phone-class network, 5-state catch-all model and 3-state catch-all model. First all models are evaluated in terms of their ability to correctly tag (miss or hit) recognition hypotheses. Then the performance of reliable combinations of these models are evaluated to show how certain reliable combinations of filler models could significantly improve the accuracy of the confidence annotation. Moreover to show the practical side of the work, an implementation of a real dialogue management system is described.