Towards a Dyadic Computational Model of Rapport Management for Human-Virtual Agent Interaction

Zhao, Ran; Papangelis, Alexandros; Cassell, Justine

doi:10.1007/978-3-319-09767-1_62

Cited by 91 publications

(57 citation statements)

References 21 publications

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“…It has been proposed that rapport should be examined at the dyadic level instead of focusing on the individual [4]. Our prior work [36] has in fact developed a dyadic computational model that explains how dyads manage rapport through the use of specific conversational strategies, which in turn function to fulfill social goals that make up rapport -face management, mutual attention, and coordination.…”

Section: Conversational Strategies Rapport and Learningmentioning

confidence: 99%

Exploring Socio-Cognitive Effects of Conversational Strategy Congruence in Peer Tutoring

Sinha

Zhao

Cassell

2015

Proceedings of the 1st Workshop on Modeling INTERPERsonal SynchrONy and infLuence

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Conversational interaction is a dynamic process in which information is conveyed and relationships are negotiated via the use and timing of specific conversational strategies. In this work we examine similarity in use and timing of the relationship-oriented communicative strategies self-disclosure, reference to shared experience and praise, during a reciprocal peer tutoring interaction. We computationally model two kinds of similarity that quantify whether and how students are similar or different in their use of the strategies over time, and differentiate the effects by gender, relationship status and session. In order to assess their impact, we leverage learning and self-reported rapport as outcome variables. Our results show significant effects in cumulative use as well as in the pattern of timings of conversational strategy usage by partners in a dyad, along with interesting relationships to socio-cognitive processes.

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Section: Conversational Strategies Rapport and Learningmentioning

confidence: 99%

Exploring Socio-Cognitive Effects of Conversational Strategy Congruence in Peer Tutoring

Sinha

Zhao

Cassell

2015

Proceedings of the 1st Workshop on Modeling INTERPERsonal SynchrONy and infLuence

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“…One is embodied or virtual tutors in collaborative settings with groups of students e.g. as in [18,31]. Knowing more about the cognitive state of the participant could help to for example better target teacher interventions.…”

Section: Motivationmentioning

confidence: 99%

Deciphering the Silent Participant

Oertel

Mora

Gustafson

et al. 2015

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

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Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, however, can provide important insights into the dynamics of the group as a whole. In this paper, we study the classification of listeners into several categories (attentive listener, side participant and bystander). We devised a thin-sliced perception test where subjects were asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as well as gaze patterns, we could identify the features which are able to distinguish between different listener categories. Moreover, the results show that many of the audiovisual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64%.

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“…The results showed that self-reported rapport is rather weakly correlated with observer-judged rapport, and also harder to predict than the latter. Zhao et al applied temporal pattern mining to extract rules for rapport management in dyads of peer-tutoring strangers and friends [47,48]. An example of such a rule indicative of high rapport in friend dyads is the verbal violation of a social norm by one interactant while in parallel her friend is smiling.…”

Section: Predicting Rapport In Dyadic Interactionsmentioning

confidence: 99%

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Müller

Huang

Bulling

2018

23rd International Conference on Intelligent User Interfaces

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Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.

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Towards a Dyadic Computational Model of Rapport Management for Human-Virtual Agent Interaction

Cited by 91 publications

References 21 publications

Exploring Socio-Cognitive Effects of Conversational Strategy Congruence in Peer Tutoring

Exploring Socio-Cognitive Effects of Conversational Strategy Congruence in Peer Tutoring

Deciphering the Silent Participant

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

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