When to elicit feedback in dialogue: Towards a model based on the information needs of speakers

Buschmeier H, Kopp S. When to elicit feedback in dialogue: Towards a model based on the information needs of speakers. In: Proceedings of the 14th International Conference on Intelligent Virtual Agents. Boston, MA, USA; 2014: 71-80.Communicative feedback in dialogue is an important mechanism that helps interlocutors coordinate their interaction. Listeners pro-actively provide feedback when they think that it is important for the speaker to know their mental state, and speakers pro-actively seek listener feedback when they need information on whether a listener perceived, understood or accepted their message. This paper presents first steps towards a model for enabling attentive speaker agents to determine when to elicit feedback based on continuous assessment of their information needs about a user's listening state

The Structure of Interpretations in Family Therapy: A Video-Enhanced Exploration *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73991/1/j.1545-5300.2000.39204.x.pd

The Face Speaks: Contextual and Temporal Sensitivity To Backchannel Responses.

Abstract. It is often assumed that one person in a conversation is active (the speaker) and the rest passive (the listeners). Conversational analysis has shown, however, that listeners take an active part in the conversation, providing feedback signals that can control conversational flow. The face plays a vital role in these backchannel responses. A deeper understanding of facial backchannel signals is crucial for many applications in social signal processing, including automatic modeling and analysis of conversations, or in the development of life-like, effective conversational agents. Here, we present results from two experiments testing the sensitivity to the context and the timing of backchannel responses. We utilised sequences from a newly recorded database of 5-minute, two-person conversations. Experiment 1 tested how well participants would be able to match backchannel sequences to their corresponding speaker sequence. On average, participants performed well above chance. Experiment 2 tested how sensitive participants would be to temporal misalignments of the backchannel sequence. Interestingly, participants were able to estimate the correct temporal alignment for the sequence pairs. Taken together, our results show that human conversational skills are highly tuned both towards context and temporal alignment, showing the need for accurate modeling of conversations in social signal processing.

Openness is Tricky

Building systems that are able to analyse communicative behaviours or take part in conversations requires a sound methodology in which the complex organisation of conversations is understood and tested on real-life samples. The data-driven approaches to human computing not only have a value for the engineering of systems, but can also provide feedback to the study of conversations between humans and between human and machines

Virtual rapport

Abstract. Effective face-to-face conversations are highly interactive. Participants respond to each other, engaging in nonconscious behavioral mimicry and backchanneling feedback. Such behaviors produce a subjective sense of rapport and are correlated with effective communication, greater liking and trust, and greater influence between participants. Creating rapport requires a tight senseact loop that has been traditionally lacking in embodied conversational agents. Here we describe a system, based on psycholinguistic theory, designed to create a sense of rapport between a human speaker and virtual human listener. We provide empirical evidence that it increases speaker fluency and engagement. 1