%0 Conference Proceedings
%T Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings
%A Victor Ponce
%A Sergio Escalera
%A Xavier Baro
%B 15th ACM International Conference on Multimodal Interaction
%D 2013
%@ 978-1-4503-2129-7
%F Victor Ponce2013
%O HuPBA;MV
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2488), last updated on Thu, 10 Nov 2016 12:25:29 +0100
%X In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions.
%U http://dx.doi.org/10.1145/2522848.2532594
%P 495-502