Measuring Mimicry in Task-Oriented Conversations: The More the Task is Difficult, The More we Mimick our Interlocutors

The tendency to unconsciously imitate others in conversations is referred to as mimicry, accommodation, interpersonal adap- tation, etc. During the last years, the computing community has made significant efforts towards the automatic detection of the phenomenon, but a widely accepted approach is still miss- ing. Given that mimicry is the unconscious tendency to imitate others, this article proposes the adoption of speaker verification methodologies that were originally conceived to spot people trying to forge the voice of others. Preliminary experiments suggest that mimicry can be detected by measuring how much speakers converge or diverge with respect to one another in terms of acoustic evidence. As a validation of the approach, the experiments show that convergence (the speakers become more similar in terms of acoustic properties) tends to appear more frequently when a task is difficult and, therefore, requires more time to be addressed

Assigning personality/identity to a chatting machine for coherent conversation generation

Endowing a chatbot with personality or an identity is quite challenging but critical to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified agent profile. We design a model consisting of three modules: a profile detector to decide whether a post should be responded using the profile and which key should be addressed, a bidirectional decoder to generate responses forward and backward starting from a selected profile value, and a position detector that predicts a word position from which decoding should start given a selected profile value. We show that general conversation data from social media can be used to generate profile-coherent responses. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.Comment: an error on author informatio

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

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.Comment: 12 pages, 6 figure