We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-be...
This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on...
We demonstrate the production of spoken output with contextually appropriate intonation in the information-state based dialogue system GoDiS. We exploit the context representation...
In this paper, we study the impact of considering context information for the annotation of emotions. Concretely, we propose the inclusion of the history of user