The idiap wolf corpus: Exploring group behaviour in a competitive role-playing game

MM'10 - Proceedings of the ACM Multimedia 2010 International Conference

Volumn , Issue , 2010, Pages 879-882

  Hung, Hayley ^a   Chittaranjan, Gokul ^b  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b IDIAP RESEARCH INSTITUTE   (Switzerland)

Author keywords

corpus; deception; human behaviour; multi party

Indexed keywords

AUDIO-VISUAL CORPORA; CORPUS; DECEPTION; FOUR-GROUP; HUMAN BEHAVIOUR; MICROPHONE ARRAYS; MULTI-PARTY; ROLE-PLAYING GAME;

MICROPHONES; VIDEO CAMERAS;

BEHAVIORAL RESEARCH;

EID: 78650990917     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1873951.1874102     Document Type: Conference Paper

References (14)

1. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, August 2006.
2. G. Chittaranjan and H. Hung. Are you a werewolf? detecting deceptive roles and outcomes in a conversational role-playing game. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2010.
3. A. De Chevigne and H. Kawahara. Yin, a fundamental frequency estimator for speech and music. Acoustical Society of America, 2002.
4. W. Dong, B. Lepri, A. Cappelletti, A. S. Pentland, F. Pianesi, and M. Zancanaro. Using the influence model to recognize functional roles in meetings. In ICMI '07: Proceedings of the 9th international conference on Multimodal interfaces, pages 271-278, New York, NY, USA, 2007. ACM.
5. D. Gatica-Perez. Automatic nonverbal analysis of social interaction in small groups: A review. Image Vision Comput., 27(12):1775-1787, 2009.
6. D. Gatica-Perez, I. McCowan, D. Zhang, and S. Bengio. Detecting group interest-level in meetings. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Mar. 2005.
7. J. Hirschberg, S. Benus, and et. al. Distinguishing deceptive and non-deceptive speech. Proc. Eurospeech, 2005.
8. D. Jayagopi, H. Hung, C. Yeo, and D. GaticaPerez. Modeling dominance in group conversations from non-verbal activity cues. Special issue on Multimedia in IEEE Trans. on Audio, Speech and Language Processing.
9. N. Mana, B. Lepri, P. Chippendale, A. Cappelletti, F. Pianesi, P. Svaizer, and M. Zancanaro. Multimodal corpus of multi-party meetings for automatic social behavior analysis and personality traits detection. In TMR '07: Proceedings of the 2007 workshop on Tagging, mining and retrieval of human related activity information, pages 9-14, New York, NY, USA, 2007. ACM.
10. N. Morgon. mrate estimater. http: //www.icsi.berkeley.edu/ftp/global/pub/ speech/morgan/, Accessed 25/07/2009.
11. K. Otsuka, H. Sawada, and J. Yamato. Automatic inference of cross-modal nonverbal interactions in multiparty conversations: "who responds to whom, when, and how?" from gaze, head gestures, and utterances. In International conference on Multimodal Interfaces, pages 255-262, New York, NY, USA, 2007. ACM.
12. A. Plotkin. Werewolf party game. http: //www.eblong.com/zarf/werewolf. html , Accessed August 27, 2009.
13. H. Salamin, S. Favre, and A. Vinciarelli. Automatic role recognition in multiparty recordings: using social affiliation networks for feature extraction. IEEE Transactions on Multimedia, 11(7):1373-1380, 2009.
14. A. Vinciarelli, M. Pantic, and H. Bourlard. Social signal processing: Survey of an emerging domain. Image and Vision Computing, December 2008.