In recent years there has been a substantial debate about the need for increasingly spontaneous, conversational corpora of spoken interaction that are not controlled or task directed. In parallel the need has arisen for the recording of multi-modal corpora which are not restricted to the audio domain alone. With a corpus that would fulfill both needs, it would be possible to investigate the natural coupling, not only in turn-taking and voice, but also in the movement of participants. In the following paper we describe the design and recording of such a corpus and we provide some illustrative examples of how such a corpus might be exploited in the study of dynamic interaction. The D64 corpus is a multimodal corpus recorded over two successive days. Each day resulted in approximately 4 h of recordings. In total five participants took part in the recordings of whom two participants were female and three were male. Seven video cameras were used of which at least one was trained on each participant. The Optitrack motion capture kit was used in order to enrich information. The D64 corpus comprises annotations on conversational involvement, speech activity and pauses as well as information of the average degree of change in the movement of participants.

This paper is concerned with modelling individual engagement and group involvement as well as their relationship in an eight-party, mutimodal corpus. We propose a number of features (presence, entropy, symmetry and maxgaze) that summarise different aspects of eye-gaze patterns and allow us to describe individual as well as group behaviour in time. We use these features to define similarities between the subjects and we compare this information with the engagement rankings the subjects expressed at the end of each interactions about themselves and the other participants. We analyse how these features relate to four classes of group involvement and we build a classifier that is able to distinguish between those classes with 71% of accuracy.

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 audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %.

In this study, we are using a multi-party recording as a template for building a parametric speech synthesiser which is able to express different levels of attentiveness in backchannel tokens. This allowed us to investigate i) whether it is possible to express the same perceived level of attentiveness in synthesised than in natural backchannels; ii) whether it is possible to increase and decrease the perceived level of attentiveness of backchannels beyond the range observed in the original corpus.

In this study, we experimentally explore the relationship between gaze direction and backchannels in face-to-face interaction. The overall motivation is to use gaze direction in a virtual agent as a mean to elicit user feedback. The relationship between gaze and backchannels was tested in an experiment in which participants were asked to provide feedback when listening to a story-telling virtual agent. When speaking, the agent shifted her gaze towards the listener at predefined positions in the dialogue. The results show that listeners are more prone to backchannel when the virtual agent’s gaze is directed towards them than when it is directed away. However, there is a high response variability for different dialogue contexts which suggests that the timing of backchannels cannot be explained by gaze direction alone.

In this paper, we present a study where a robot instructs a human on how to draw a route on a map. The human and robot are seated face-to-face with the map placed on the table between them. The user's and the robot's gaze can thus serve several simultaneous functions: as cues to joint attention, turn-taking, level of understanding and task progression. We have compared this face-to-face setting with a setting where the robot employs a random gaze behaviour, as well as a voice-only setting where the robot is hidden behind a paper board. In addition to this, we have also manipulated turn-taking cues such as completeness and filled pauses in the robot's speech. By analysing the participants' subjective rating, task completion, verbal responses, gaze behaviour, and drawing activity, we show that the users indeed benefit from the robot's gaze when talking about landmarks, and that the robot's verbal and gaze behaviour has a strong effect on the users' turn-taking behaviour. We also present an analysis of the users' gaze and lexical and prosodic realisation of feedback after the robot instructions, and show that these cues reveal whether the user has yet executed the previous instruction, as well as the user's level of uncertainty.

In the last couple of years more and more multimodal corpora have been created. Recently many of these corpora have also included RGB-D sensors' data. However, there is to our knowledge no publicly available corpus, which combines accurate gaze-tracking, and high- quality audio recording for group discussions of varying dynamics. With a corpus that would fulfill these needs, it would be possible to investigate higher level constructs such as group involvement, individual engagement or rapport, which all require multimodal feature extraction. In the following paper we describe the design and recording of such a corpus and we provide some illustrative examples of how such a corpus might be exploited in the study of group dynamics.