Modelling Engagement in Multi-PartynConversations: Data-Driven Approaches to Understanding Human-Human Communication Patterns for Use in Human-Robot Interactions

Abstract

The aim of this thesis is to study human-human interaction in order to provide virtual agents and robots with the capability to engage into multi-party-conversations in a human-like-manner. The focus lies with the modelling of conversational dynamics and the appropriate realization of multi-modal feedback behaviour. For such an undertaking, it is important to understand how human-human communication unfolds in varying contexts and constellations over time. To this end, multi-modal human-human corpora are designed as well as annotation schemes to capture conversational dynamics are developed. Multi-modal analysis is carried out and models are built. Emphasis is put on not modelling speaker behaviour in general and on modelling listener behaviour in particular.

In this thesis, a bridge is built between multi-modal modelling of conversational dynamics on the one hand multi-modal generation of listener behaviour in virtual agents and robots on the other hand. In order to build this bridge, a unit-selection multi-modal synthesis is carried out as well as a statistical speech synthesis of feedback. The effect of a variation in prosody of feedback token on the perception of third-party observers is evaluated. Finally, the effect of a controlled variation of eye-gaze is evaluated, as is the perception of user feedback in human-robot interaction.​