Audiovisual Behavior Descriptors for Depression Assessment (bibtex)
by Scherer, Stefan, Stratou, Giota and Morency, Louis-Philippe
Abstract:
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.
Reference:
Audiovisual Behavior Descriptors for Depression Assessment (Scherer, Stefan, Stratou, Giota and Morency, Louis-Philippe), In Proceedings of ICMI'13, ACM Press, 2013.
Bibtex Entry:
@inproceedings{scherer_audiovisual_2013,
	address = {Sydney, Australia},
	title = {Audiovisual {Behavior} {Descriptors} for {Depression} {Assessment}},
	isbn = {978-1-4503-2129-7},
	url = {http://ict.usc.edu/pubs/Audiovisual%20behavior%20descriptors%20for%20depression%20assessment.pdf},
	doi = {10.1145/2522848.2522886},
	abstract = {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.},
	booktitle = {Proceedings of {ICMI}'13},
	publisher = {ACM Press},
	author = {Scherer, Stefan and Stratou, Giota and Morency, Louis-Philippe},
	month = dec,
	year = {2013},
	keywords = {Virtual Humans, UARC},
	pages = {135--140}
}
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