Fabio Pianesi
Massimo Zancanaro
ABSTRACT
This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.
- André, E., Klense, M., Gebhard, P., Allen, S., Rist, T. Integrating models of personality and emotions into lifelike characters. In Proceedings of the Workshop on Affect in Interaction - Towards a New Generation of Interfaces, pp 136--149, 1999. Google Scholar
Digital Library
- Ambady, N., and Rosenthal, R. Thin slices of expressive behaviors as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin, 111, pp. 256--274Google ScholarCross Ref
- Argamon, S., Dhawle, S., Koppel, M., Pennbaker, J. Lexical predictors of personality type. In Proceedings of Interface and the Classification Society of North America. 2005.Google Scholar
- Blanck, P. D., Rosenthal, R., Vannicelli, M., and Lee, T. D. Therapists' tone of voice: descriptive, psychometric, interactional and competence analyses. Journal of Social and Clinical Psychology, 4, pp. 154--178. 1986Google ScholarCross Ref
- Burges, C. J. C., A Tutorial on Support Vector Machines for Pattern Recognition. In Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121--167, 1998. Google ScholarDigital Library
- Carrere, S., and Gottman, J. M. Predicting divorce among newlyweds from the first three minutes of a marital conflict discussion. Family Process, 38, pp. 293--301Google ScholarCross Ref
- Castelfranchi, F. D. Rosis. How can personality factors contribute to make agents more 'believable', Proceedings of the Workshop on Behavior Planning for Lifelike Characters and Avatars, Sitges, Spain, 25--35, 1999.Google Scholar
- Chippendale, P. Towards Automatic Body Language Annotation. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition - FG2006 (IEEE), pp. 487--492, Southampton, UK, 2006. Google ScholarDigital Library
- Curhan, J. R., and Pentland, A. Thin slices of Negotiation: predicting outcomes from conversational dynamics within the first 5 minutes. Journal of Applied Psychology, Vol. 92, No. 3, pp. 802--811, 2007Google ScholarCross Ref
- Farma, T., and Cortivonis, I. Un Questionario sul "Locus of Control": Suo Utilizzo nel Contesto Italiano (A Questionnaire on the Locus of Control: Its Use in the Italian Context). Ricerca in Psicoterapia. Vol. 2, 2000.Google Scholar
- Furnham, D. Language and Personality. In Giles, H., & Robinson, W. (Eds.), Handbook of Language and Social Psychology. Winley, 1990.Google Scholar
- Giannoutsos, V. 'Stress, Attitudes and Personality in Computing'. In Proceedings of ETHICOMP 2004, Syros, Gr. 2004Google Scholar
- Gottman, J. M, and Notarius, C. I. Decade review: observing marital interaction. Journal of Marriage and the Family, 62, pp. 927--947, 2000Google ScholarCross Ref
- Goren-Bar, D., Graziola, I., Pianesi, F., Zancanaro, M. Influence of Personality Factors on Visitors' Attitudes towards Adaptivity Dimensions for Mobile Museum Guides'. In User Modeling and User Adapted Interaction: The Journal of Personalization Research, 16 (1), 31--62. 2006 Google ScholarDigital Library
- Hall, J. W., and Watson, W. H. The Effects of a normative intervention on group decision-making performance. In Human Relations, 23(4), 299--317, 1970.Google ScholarCross Ref
- Heider, F. The psychology of interpersonal relations. Wiley. New York. 1957Google Scholar
- Hsu, C. W. A. Hsu C.-W., Lin C.-J. A Comparison of Methods for Multi-Class Support Vector Machines. IEEE Transactions on Neural Networks, 13, pp. 415--425, 2002. Google ScholarDigital Library
- John, O. P., Srivastava, S. The Big five trait taxonomy: History, measurement and theoretical perspectives. In Pervian, L. A. & John, O. P., (Eds.) Handbook of personality theory and research. Guilford Press. New York, 1999.Google Scholar
- Johnson, R. D., Marakas, G., Plamer, J. W. Individual Perceptions Regarding the Capabilities and Roles of Computing Technology: Development of The Computing Technology Continuum of Perspective. Ms. 2002.Google Scholar
- Kenny, D. A. Interpersonal perception: A social relations analysis. New York: Guilford Press. 1994.Google Scholar
- Kressel, U. Pairwise Classification and Support Vector Machines. In B. Scholkopf, C. J. C. Burges, and A. J. Smola (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA, 1999. Google ScholarDigital Library
- Mairesse, F., Walker, M. Words marks the nerds: computational models of personality recognition through language. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp 543--548, 2006.Google Scholar
- Mairesse F., Walker M. A., Mehl M. R., and Moore R. K. Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. In Journal of Artificial Intelligence Research 30, pp.457--500. 2007 Google ScholarDigital Library
- Mallory P., and Miller V. A possible basis for the association of voice characteristics and personality traits. Speech Monograph, 25, pp. 255--260, 1958.Google ScholarCross Ref
- Mana, N., Lepri, B., Chippendale, P., Cappelletti, A., Pianesi, F., Svaizer, P, and Zancanaro, M. Multimodal Corpus of Multi-Party Meetings for Automatic Social Behavior Analysis and Personality Traits Detection. In Proceedings of Workshop on Tagging, Mining and Retrieval of Human-Related Activity Information, at ICMI07, International Conference on Multimodal Interfaces, Nagoya, Japan, 2007. Google ScholarDigital Library
- Oberlander, J. and Nowson, S. 2006. Whose thumb is it anyway? Classifying author personality from weblog text. In Proceedings of the Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, 627--634, 2006. Google ScholarDigital Library
- Pentland, A. Socially Aware Computation and Communication. IEEE Computer, May 2005. Google ScholarDigital Library
- Pentland, A. A Computational Model of Social Signaling. Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06). Volume 1, Issue 2006, Page(s):1080--1083, 2006. Google ScholarDigital Library
- Pentland, A. Honest Signals: how they shape our world. (In press). MIT Press, September 2008. Google ScholarDigital Library
- Perugini, M. and Di Blas L. Analyzing Personality-Related Adjectives from an Eticemic Perspective: the Big Five Marker Scale (BFMS) and the Italian AB5C Taxonomy. In De Raad, B., & Perugini, M. (Eds.), Big Five Assessment, Hogrefe und Huber Publishers. Gööttingen, 281--304, 2002.Google Scholar
- Rotter J. B. Generalized Expectancies for Internal versus External Control of Reinforcment. In Psychological Monographs, 80 (1, Whole N. 609), 1965Google Scholar
- Siegman A., and Pope B. Personality variable associated with productivity and verbal fluency in the initial interview. Proceedings of the 73rd Annual Conference of the American Psychological Association, 1965.Google ScholarCross Ref
- Scherer K. R. Personality Inference from Voice Quality: the Loud Voice of Extraversion. European Journal of Social Psychology, vol. 8, pp. 467--487, 1978.Google ScholarCross Ref
- Scherer K. R. Personality markers in speech. In Scherer K. R. and Giles H. (eds.) Social Markers in Speech, pp. 147--209 Cambridge University Press, 1979.Google Scholar
- Stoltzman, W. Toward a Social Signaling Framework: Activity and Emphasis in Speech. MEng Thesis, MIT, 2006Google Scholar
- Zhou, X. and Conati, C. Inferring user goals from personality and behavior in a causal model of user affect. In Proceedings of the 8th international Conference on intelligent User interfaces (Miami, Florida, USA, January 12 - 15, 2003). IUI '03. ACM, New York, NY, 211--218. DOI= http://doi.acm.org/10.1145/604045.604078, 2003. Google ScholarDigital Library
Index Terms
- Multimodal recognition of personality traits in social interactions
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