Fusing affective dimensions and audio-visual features from segmented video for depression recognition INAOE-BUAP's participation at AVEC'14 Challenge

AVEC 2014 - Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge, Workshop of MM 2014

Volumn , Issue , 2014, Pages 49-55

  Espinosa, Humberto Pérez ^a   Escalante, Hugo Jair ^b   Villaseñor Pineda, Luis ^b   Montes Y gómez, Manuel ^b   Pinto Avedaño, David ^a   Reyes Meza, Veronica ^c  

^a BENEMÉRITA UNIVERSIDAD AUTÓNOMA DE PUEBLA   (Mexico)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

^c UPAEP University   (Mexico)

Author keywords

Depression recognition; Depressive disorder; Meta classifier; Multimodal information processing

Indexed keywords

CLASSIFICATION (OF INFORMATION); DISEASE CONTROL; FEATURE EXTRACTION; MEDICAL COMPUTING; PARTIAL DISCHARGES;

AUDIO-VISUAL FEATURES; AUDIO-VISUAL INFORMATION; DEPRESSION RECOGNITION; DEPRESSIVE DISORDER; META-CLASSIFIERS; MULTI-MODAL INFORMATION; RECOGNITION MODELS; SEGMENTATION PROCEDURE;

HUMAN COMPUTER INTERACTION;

EID: 84919384869     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2661806.2661815     Document Type: Conference Paper

References (15)

1. A. Batliner, D. Seppi, S. Steidl, and Björn Schuller. Segmenting into adequate units for automatic recognition ofemotion-related episodes: A speech-based approach. Advances in Human-Computer Interaction, 2010(Article ID 782802):15 pages, 2010.
2. A. Beck, R. Steer, R. Ball, and W. Ranieri. Comparison of Beck depression inventories -ia and -ii in psychiatric outpatients. Journal of Personality Assessment, 67(3):588-597, 1996.
3. Paul Boersma and David Weenink. Praat: doing phonetics by computer. July 2010.
4. J. S. Buyukdura, S. M. McClintock, and P. E. Croarkin. Psychomotor retardation in depression: Biological underpinnings, measurement, and treatment. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 35(2):395-409, 2011.
5. Nivja H. de Jong and Ton Wempe. Praat script to detect syllable nuclei and measure speech rate automatically. Behavior Research Methods, 41(2):385-390, 2009.
6. I. Guyon, V. Athitsos, P. Jangyodsuk, H. J. Escalante, and B. Hamner. Results and analysis of the chalearn gesture challenge 2012. In WDIA: Advances in Depth Image Analysis and Applications, volume 7854 of LNCS, pages 186-204. Springer, 2013.
7. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explorations, 11(1), 2009.
8. Kenji Kira and Larry A. Rendell. A practical approach to feature selection. pages 249-256, 1992.
9. World Health Organization. Depression - a hidden burden. who flyer. http://www.who.int/topics/depression/en/, Link verified on July 15, 2014 2012.
10. C. E. Rasmussen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
11. Klaus R. Scherer. Psychological Models of Emotion, chapter 6, pages 137-162. Oxford University Press, 2000.
12. B. Schölkopf and A. J. Smola. Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
13. M. Valstar, B. Schuller, K. Smith, T. Almaev, F. Eyben, J. Krajewski, R. Cowie, and M. Pantic. AVEC 2014 - 3D dimensional affect and depression recognition challenge. In Proc. of the 4th ACM international workshop on Audio/visual emotion challenge, 2014.
14. M. F. Valstar, B. Schuller, K. Smith, F. Eyben, B. Jiang, S. Bilakhia, S. Schnieder, R. Cowie, and M. Pantic. AVEC 2013 - the continuous audio/visual emotion and depression recognition challenge. In 3-10, editor, Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge, 2013.
15. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Vision and Pattern Recognition Conference, volume 1, pages 511-518, 2001.