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27 - Social Signal Processing for Conflict Analysis and Measurement

from Part IV - Applications of Social Signal Processing

Published online by Cambridge University Press:  13 July 2017

Alessandro Vinciarelli
University of Glasgow
Judee K. Burgoon
University of Arizona
Nadia Magnenat-Thalmann
Université de Genève
Maja Pantic
Imperial College London
Alessandro Vinciarelli
University of Glasgow
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The literature proposes several definitions of conflict: “a process in which one party perceives that its interests are being opposed or negatively affected by another party” (Wall & Roberts Callister, 1995); “[conflict takes place] to the extent that the attainment of the goal by one party precludes its attainment by the other” (Judd, 1978); “[…] the perceived incompatibilities by parties of the views, wishes, and desires that each holds” (Bell & Song, 2005); and so on. While apparently different, all definitions share a common point, that is, the incompatibility between goals and targets pursued by different individuals involved in the same interaction.

Following the definitions above, conflict is a phenomenon that cannot be observed directly (goals and targets are not accessible to our senses), but only inferred from observable behavioural cues. Therefore, the phenomenon appears to be a suitable subject for a domain like social signal processing that includes detection and interpretation of observable social signals among its research focuses (Vinciarelli et al., 2008; Vinciarelli, Pantic, & Bourlard, 2009; Vinciarelli, Pantic et al., 2012). Furthermore, the literature shows that emotions are ambiguous conflict markers – people tend to display both positive and negative emotions with widely different levels of arousal (Arsenio & Killen, 1996) – while social signals are more reliable markers of conflict (Gottman, Markman, & Notarius, 1977; Sillars et al., 1982; Cooper, 1986; Smith-Lovin & Brody, 1989; Schegloff, 2000).

One of the main challenges toward the development of automatic conflict analysis approaches is the collection of ecologically valid data (Vinciarelli, Kim et al., 2012). The main probable reason is that there is no conflict in absence of real goals and motivations, but these are difficult to produce in laboratory experiments. To the best of our knowledge, the few corpora where the subjects are moved by real motivations and, hence, actually experience conflict are collections of political debates (Vinciarelli, Kim et al., 2012) and recordings of counseling sessions for couples in distress (Black et al., 2013). However, while the former can be distributed publicly and have even been used in international benchmarking campaigns (Schuller et al., 2013), the latter are protected for privacy reasons.

Social Signal Processing , pp. 379 - 388
Publisher: Cambridge University Press
Print publication year: 2017

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Arsenio, W. F. & Killen, M. (1996). Conflict-related emotions during peer disputes.Early Education and Development, 7(1), 43–57.Google Scholar
Bell, C. & Song, F. (2005). Emotions in the conflict process: An application of the cognitive appraisal model of emotions to conflict management.International Journal of Conflict Management, 16(1), 30–54.Google Scholar
Black, M. P., Katsamanis, A., Baucom, B. R., et al. (2013). Toward automating a human behavioral coding system for married couples’ interactions using speech acoustic features.Speech Communication, 55(1), 1–21.Google Scholar
Bousmalis, K., Mehu, M., & Pantic, M. (2013). Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases and tools.Image and Vision Computing, 31(2), 203–221.Google Scholar
Bousmalis, K., Morency, L. P., & Pantic, M. (2011). Modeling Hidden Dynamics of Multimodal Cues for Spontaneous Agreement and Disagreement Recognition. In Proceedings of IEEE International Conference on Automatic Face and Gesture Recogition (pp. 746–752).
Cooper, V. W. (1986). Participant and observer attribution of affect in interpersonal conflict: An examination of noncontent verbal behavior.Journal of Nonverbal Behavior, 10(2), 134–144.Google Scholar
Cristani, M., Pesarin, A., Drioli, C., et al. (2011). Generative modeling and classification of dialogs by a low-level turn-taking feature.Pattern Recognition, 44(8), 1785–1800.Google Scholar
D’Errico, F., Poggi, I., Vinciarelli, A., & Vincze, L. (Eds). (2015). Conflict and Multimodal Communication. Berlin: Springer.
Galley, M., McKeown, K., Hirschberg, J., & Shriberg, E. (2004). Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 669–676).
Georgiou, P. G., Black, M. P., Lammert, A. C., Baucom, B. R., & Narayanan, S. S. (2011). “That’s Aggravating, Very Aggravating”: Is It Possible to Classify Behaviors in Couple Interactions Using Automatically Derived Lexical Features? In Proceedings of International Conference on Affective Computing and Intelligent Interaction (pp. 87–96).
Germesin, S. & Wilson, T. (2009). Agreement detection in multiparty conversation. In Proceedings of ACM International Conference on Multimodal Interfaces (pp. 7–14).
Gottman, J., Markman, H., & Notarius, C. (1977). The topography of marital conflict: A sequential analysis of verbal and nonverbal behavior.Journal of Marriage and the Family, 39(3), 461–477.Google Scholar
Grezes, F., Richards, J., & Rosenberg, A. (2013). Let me finish: Automatic conflict detection using speaker overlap. In Proceedings of 14th Annual Conference of the International Speech Communication Association (pp. 200–204).
Hillard, D., Ostendorf, M., & Shriberg, E. (2003). Detection of agreement vs. disagreement in meetings: Training with unlabeled data. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 34–36).
Joni, S. N. & Beyer, D. (2009). How to pick a good fight.Harvard Business Review, 87(12), 48–57.Google Scholar
Judd, C. M. (1978). Cognitive effects of attitude conflict resolution.Journal of Conflict Resolution, 22(3), 483–498.Google Scholar
Kim, S., Filippone, M., Valente, F., & Vinciarelli, A. (2012). Predicting the conflict level in television political debates: An approach based on crowdsourcing, nonverbal communication and Gaussian processes. In Proceedings of the ACM International Conference on Multimedia (pp. 793–796).
Kim, S., Valente, F., Filippone, M., & Vinciarelli, A. (2014). Predicting continuous conflict perception with Bayesian Gaussian processes.IEEE Transactions on Affective Computing, 5(2), 187–200.Google Scholar
Kim, S., Valente, F., & Vinciarelli, A. (2012). Automatic detection of conflicts in spoken conversations: Ratings and analysis of broadcast political debates. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 5089–5092).
Perina, A., Cristani, M., Castellani, U., Murino, V., & Jojic, N. (2009). Free energy score space. In Advances in Neural Information Processing Systems (pp. 1428–1436).
Pesarin, A., Cristani, M., Murino, V., & Vinciarelli, A. (2012). Conversation analysis at work: Detection of conflict in competitive discussions through automatic turn-organization analysis.Cognitive Processing, 13(2), 533–540.Google Scholar
Pieraccini, R. (2012). The Voice in the Machine: Building Computers that Understand Speech. Cambridge, MA: MIT Press.
Poggi, I., D’Errico, F., & Vincze, L. (2011). Agreement and its multimodal communication in debates: A qualitative analysis.Cognitive Computation, 3(3), 466–479.Google Scholar
Räsänen, O. & Pohjalainen, J. (2013). Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech. In Proceedings of 14th Annual Conference of the International Speech Communication Association (pp. 210– 214).
Schegloff, E. (2000). Overlapping Talk and the Organization of Turn-taking for Conversation.Language in Society, 29(1), 1–63.Google Scholar
Schuller, B., Steidl, S., Batliner, A., et al. (2013). The InterSpeech 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism. In Proceedings of 14th Annual Conference of the International Speech Communication Association (pp. 148–152).
Sillars, A. L., Coletti, S. F., Parry, D., & Rogers, M. A. (1982). Coding verbal conflict tactics: Nonverbal and perceptual correlates of the “avoidance-distributive-integrative” distinction.Human Communication Research, 9(1), 83–95.Google Scholar
Smith-Lovin, L. & Brody, C. (1989). Interruptions in group discussions: The effects of gender and group composition.American Sociological Review, 54(3), 424–435.Google Scholar
Vinciarelli, A, Dielmann, A, Favre, S, & Salamin, H. (2009). Canal9: A database of political debates for analysis of social interactions. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction(vol. 2, pp. 96–99).Google Scholar
Vinciarelli, A., Kim, S., Valente, F., & Salamin, H. (2012). Collecting data for socially intelligent surveillance and monitoring approaches: The case of conflict in competitive conversations. In Proceedings of International Symposium on Communications, Control and Signal Processing (pp. 1–4).
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009). Social signal processing: Survey of an emerging domain.Image and Vision Computing, 27(12), 1743–1759.Google Scholar
Vinciarelli, A., Pantic, M., Bourlard, H., & Pentland, A. (2008). Social signal processing: State of the art and future perspectives of an emerging domain. In Proceedings of the ACM International Conference on Multimedia (pp. 1061–1070).
Vinciarelli, A., Pantic, M., Heylen, D., et al. (2012). Bridging the gap between social animal and unsocial machine: A survey of social signal processing.IEEE Transactions on Affective Computing, 3(1), 69–87.Google Scholar
Wall, J. A. & Roberts Callister, R. (1995). Conflict and its management.Journal of Management, 21(3), 515–558.Google Scholar
Wrede, B. & Shriberg, E. (2003a). Spotting “hotspots” in meetings: Human judgments and prosodic cues. In Proceedings of Eurospeech (pp. 2805–2808).
Wrede, B. & Shriberg, E. (2003b). The relationship between dialogue acts and hot spots in meetings. In Proceedings of the IEEE Speech Recognition and Understanding Workshop (pp. 180– 185).

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