In this paper we present three approaches towards adaptive speech understanding. The target system is a model-based Adaptive Spoken Dialogue Manager, the OwlSpeak ASDM. We enhanced this system in order to properly react on non-understandings in real-life situations where intuitive communication is required. OwlSpeak provides a model-based spoken interface to an Intelligent Environment depending on and adapting to the current context. It utilises a set of ontologies used as dialogue models that can be combined dynamically during runtime. Besides the benefits the system showed in practice, real-life evaluations also conveyed some limitations of the model-based approach. Since it is unfeasible to model all variations of the communication between the user and the system beforehand, various situations where the system did not correctly understand the user input have been observed. Thus we present three enhancements towards a more sophisticated use of the ontology-based dialogue models and show how grammars may dynamically be adapted in order to understand intuitive user utterances. The evaluation of our approaches revealed the incorporation of a lexical-semantic knowledgebase into the recognition process to be the most promising approach.

In this work we show that there is a need of using multimodal resources during human-computer interaction (HCI) in intelligent systems. We propose that not only creating multimodal output for the user is important, but to take multimodal input resources into account for the decision when and how to interact. Especially the use of multimodal input resources for the decision when and how to provide assistance in HCI is important. The use of assistive functionalities like providing adaptive explanations to keep the user motivated and cooperative is more than a side-effect and demands a closer look. In this paper we introduce our approach on how to use multimodal input ressources in an adaptive and generic explanation pipeline. We do not only concentrate on using explanations as a way to manage user knowledge, but to maintain the cooperativeness, trust and motivation of the user to continue a healthy and well-structured HCI.

We describe an experimentalWizard-of-Oz-setup for the integration of emotional strategies into spoken dialogue management. With this setup we seek to evaluate different approaches to emotional dialogue strategies in human computer interaction with a spoken dialogue system. The study aims to analyse what kinds of emotional strategies work best in spoken dialogue management especially facing the problem that users may not be honest about their emotions. Therefore as well direct (user is asked about his state) as indirect (measurements of psychophysiological features) evidence is considered for the evaluation of our strategies.