Glottal wave analysis with Pitch Synchronous Iterative Adaptive Inverse Filtering
Speech Communication - Eurospeech '91
SmartBody: behavior realization for embodied conversational agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Face detection and tracking in video sequences using the modifiedcensus transformation
Image and Vision Computing
3D Constrained Local Model for rigid and non-rigid facial tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Speech and Language
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We investigate audiovisual indicators, in particular measures of reduced emotional expressivity and psycho-motor retardation, for depression within semi-structured virtual human interviews. Based on a standard self-assessment depression scale we investigate the statistical discriminative strength of the audiovisual features on a depression/no-depression basis. Within subject-independent unimodal and multimodal classification experiments we find that early feature-level fusion yields promising results and confirms the statistical findings. We further correlate the behavior descriptors with the assessed depression severity and find considerable correlation. Lastly, a joint multimodal factor analysis reveals two prominent factors within the data that show both statistical discriminative power as well as strong linear correlation with the depression severity score. These preliminary results based on a standard factor analysis are promising and motivate us to investigate this approach further in the future, while incorporating additional modalities.