Social signal processing, the detection and interpretation of temporal patterns of non—verbal behavioral cues in a given context, is a natural and, for the most part, unconscious process for humans. This process remains a rather difficult task for machines, but it is an important one to achieve if the goal is to realize a naturalistic and efficient human-computer interaction.
Agreements and disagreements are social attitudes that occur daily and are inevitable in a variety of everyday situations, from everyday simple discussions over, e.g., which restaurant to dine in, to traditionally controversial topics like politics. As a first step towards realizing the goal of a socially sensitive machine, we have focused our efforts towards devising a way to automatically detect these social attitudes of agreement and disagreement.
This work is our first attempt towards the automatic detection of spontaneous agreement and disagreement . Related work has used only verbal and prosodic features. This work-in-progress attempts to show that:
The data used are video segments annotated as agreement or disagreement, from the Canal9 Political Debates, a database of real televised debates, containing highly spontaneous data. Only episodes of direct (dis)agreement are used, i.e. segments during which the speaker specifically states her agreement or disagreement. The cues used are the relevant head and hand gestures, as those are listed in our survey on (dis)agreement, which have been manually annotated in all the episodes of agreement and disagreement we use, and the prosodic features used in related work (fundamental frequency and energy).
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A. Vinciarelli, M. Pantic, H. Bourlard, A. Pentland. Proceedings of ACM Int'l Conf. Multimedia (MM'08). Vancouver, Canada, pp. 1061 - 1070, October 2008.