The Attention-Hesitation Model. A Non-Intrusive Intervention Strategy for Incremental Smart Home Dialogue Management
Creators: name: Richter, Birte
Publisher: name: Universität Bielefeld
Year: 2021
Subjects: subject: 000

description: Smart homes are one of the most emergent research fields and provide fundamentally new means of interaction. So-called Smart Personal Assistants (SPAs) entered the household and assist us in our daily activities. Currently, these agents do not react to the attention of the smart home user. However, from Human-Human Interaction (HHI) research we know that humans coordinate their speech and adapt their behavior continuously, based on their interaction partner’s actions and reactions. Therefore, the central question I ask in this dissertation is how human attention can be incorporated into dialogue management, to improve Human-Agent Interaction (HAI) in smart homes. Research shows that speakers’ hesitations are often produced as a reaction to the listener’s inattentiveness in HHI. Furthermore, they can improve the listeners’ comprehension. Therefore, I investigate whether it is possible to use system hesitations, based on the attention of the human interaction partner, as a communicative act for dialogue coordination in HAI within a smart-home environment. To this end, I develop a theoretical model based on observations from HHI, implement it in an autonomous agent and evaluate it in five interaction studies. This document consists of three parts. In the first part, I develop a model which allows the dialogue management to incorporate the human attention: the Attention-Hesitation Model (AHM). The model uses system hesitations as a non-intrusive intervention strategy to coordinate the human attention with system speech. This theoretical model is based on interdisciplinary literature from HHI and HAI research. In the second part, I elaborate on the technical requirements implied by the integration of the AHM in an autonomous system. A technical realization of an incremental dialogue system in presented. Two main concepts for dialogue modeling are identified: (1) the use of interaction patterns with system task descriptions for generalizability and (2) the concept of the IU model to deal with the incremental nature of human dialogue. With the combination of the frameworks Pamini and inprotk both concepts are considered in my dialogue system. This allows autonomous HAI and the investigation of the effects of my AHM in interaction. In the third part, I evaluate the effects of my AHM on the interaction (partner) in five Evaluation Cycles (ECs), consisting of three pilot- and two HAI studies in a smart-home environment. In these cycles, I further enhance my model, its implementation, and the experimental design. Thereby, I investigate the effect of the AHM on the task performance and the side effects in interaction: the subjective ratings of the agent and the visual attention of interlocutors. With my investigations, I show that in short interactions without a change of discourse, the participants interacting with an agent that uses my AHM are significantly less inattentive than participants in the baseline (EC1). Furthermore, I show that the AHM can work fully autonomously (EC2, EC4). Regarding the task performance, I demonstrate that participants interacting with an agent that uses my AHM perform significantly better in some practical tasks than participants in the baseline (EC3-EC5). This effect is, however, accompanied by lower subjective ratings of the agent (EC2-EC4). The ratings show that repetitions can be perceived as annoying (EC2) and users may struggle with the differentiation of unfilled pauses from turn-ends in more complex scenarios (EC2, EC3). However, the use of lengthening may counteract this problem and enhance some subjective ratings (EC4). The final model uses mutual gaze and task related features to distinguish inattentiveness based on (1) missing engagement from (2) difficulties in understanding. To deal with inattention based on missing engagement, a cascade of lengthening, unfilled pauses, and hesitation vowels are used. For difficulties in understanding, the model uses repetitions with lengthening. This combination improves the task performance without negative side effects on the interaction (EC5).