Abstract

Analysis of audiovisual human behavior observations is a common practice in behavioral sciences. It is generally carried through by expert annotators who are asked to evaluate several aspects of the observations along various dimensions. This can be a tedious task. We propose that automatic classification of behavioral patterns in this context can be viewed as a multiple instance learning problem. In this paper, we analyze a corpus of married couples interacting about a problem in their relationship. We extract features from both the audio and the transcriptions and apply the Diverse Density-Support Vector Machine framework. Apart from attaining classification on the expert annotations, this framework also allows us to estimate salient regions of the complex interaction.

Date

2011

Authors

Athanasios Katsamanis, James Gibson, Matthew P Black, Shrikanth S Narayanan

Conference

Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I 4

Pages

145-154

Publisher

Springer Berlin Heidelberg