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1 - Introduction: Social Signal Processing

Published online by Cambridge University Press:  13 July 2017

Alessandro Vinciarelli
Affiliation:
University of Glasgow
Judee K. Burgoon
Affiliation:
University of Arizona
Nadia Magnenat-Thalmann
Affiliation:
Université de Genève
Maja Pantic
Affiliation:
Imperial College London
Alessandro Vinciarelli
Affiliation:
University of Glasgow
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Summary

Introduction

Social signal processing (SSP) is the computing domain aimed at modeling, analysis, and synthesis of social signals in human–human and human–machine interactions (Pentland, 2007; Vinciarelli et al., 2008, 2012; Vinciarelli, Pantic, & Bourlard, 2009). According to different theoretic orientations, social signals can be defined in different ways, for example, “acts or structures that influence the behavior or internal state of other individuals” (Mehu & Scherer, 2012; italics in original), “communicative or informative signals which … provide information about social facts” (Poggi & D'Errico, 2012; italics in original), or “actions whose function is to bring about some reaction or to engage in some process” (Brunet & Cowie, 2012; italics in original). The definitions might appear different, but there seems to be consensus on at least three points.

  1. • Social signals are observable behaviors that people display during social interactions.

  2. • The social signals of an individual A produce changes in others (e.g., the others develop an impression or a belief about A, react to A with appropriate social signals, or coordinate their social signals with those of A).

  3. • The changes produced by the social signals of A in others are not random, but follow principles and laws.

In a computing perspective, the observations above lead to the key idea that shapes the field of Social Signal Processing, namely that social signals are the physical, machine detectable trace of social and psychological phenomena not otherwise accessible to direct observation. In fact, SSP addresses the following three main problems.

  1. Modeling: identification of principles and laws that govern the use of social signals.

  2. Analysis: automatic detection and interpretation of social signals in terms of the principles and laws above.

  3. Synthesis: automatic generation of artificial social signals following the principles and laws above.

Correspondingly, this book is organized into four main sections of which the first three focus on the three problems outlined above while the fourth one introduces current applications of SSP technologies.

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Chapter
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Publisher: Cambridge University Press
Print publication year: 2017

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References

Brunet, P. & Cowie, R. (2012). Towards a conceptual framework of research on social signal processing. Journal of Multimodal User Interfaces, 6(3–4), 101–115.Google Scholar
Mehu, M. & Scherer, K. (2012). A psycho-ethological approach to social signal processing. Cognitive Processing, 13(2), 397–414.Google Scholar
Pentland, A. (2007). Social signal processing. IEEE Signal Processing Magazine, 24(4), 108–111.Google Scholar
Poggi, I. & D'Errico, F. (2012). Social signals: A framework in terms of goals and beliefs. Cognitive Processing, 13(2), 427–445.Google Scholar
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009). Social signal processing: Survey of an emerging domain. Image and Vision Computing Journal, 27(12), 1743–1759.Google Scholar
Vinciarelli, A., Pantic, M., Bourlard, H., & Pentland, A. (2008). Social signal processing: Stateof- the-art and future perspectives of an emerging domain. Proceedings of the ACM International Conference on Multimedia (pp. 1061–1070). New York: Association for Computing Machinery.
Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., & Schroeder, M. (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
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