PT Unknown
AU Victor Ponce
   Sergio Escalera
   Xavier Baro
TI Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings
BT 15th ACM International Conference on Multimodal Interaction
PY 2013
BP 495
EP 502
DI 10.1145/2522848.2532594
AB 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.
ER