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Social Signal Processing: Survey of an Emerging Domain
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 153 (32 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Bridging the Gap Between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
- IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
"... Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This article is the first survey of the domain that jointly considers its three major aspects, namely modeling, analysis and synthesis of social behaviour. Modeling investigate ..."
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Cited by 35 (7 self)
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Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This article is the first survey of the domain that jointly considers its three major aspects, namely modeling, analysis and synthesis of social behaviour. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behaviour via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
Social signals, their function, and automatic analysis: a survey
- In Proceedings of the International Conference on Multimodal interfaces
, 2008
"... ABSTRACT Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitu ..."
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Cited by 14 (2 self)
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ABSTRACT Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes towards other human (and virtual) participants in the current social context. As such, SSP integrates both engineering (speech analysis, computer vision, etc.) and human sciences (social psychology, anthropology, etc.) as it requires multimodal and multidisciplinary approaches. As of today, SSP is still in its early infancy, but the domain is quickly developing, and a growing number of works is appearing in the literature. This paper provides an introduction to nonverbal behaviour involved in social signals and a survey of the main results obtained so far in SSP. It also outlines possibilities and challenges that SSP is expected to face in the next years if it is to reach its full maturity.
Social Signal Processing: Understanding Social Interactions through Nonverbal Behavior Analysis
"... This paper introduces Social Signal Processing (SSP), the domain aimed at automatic understanding of social interactions through analysis of nonverbal behavior. The core idea of SSP is that nonverbal behavior is machine detectable evidence of social signals, the relational attitudes exchanged betwee ..."
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Cited by 13 (1 self)
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This paper introduces Social Signal Processing (SSP), the domain aimed at automatic understanding of social interactions through analysis of nonverbal behavior. The core idea of SSP is that nonverbal behavior is machine detectable evidence of social signals, the relational attitudes exchanged between interacting individuals. Social signals include (dis-)agreement, empathy, hostility, and any other attitude towards others that is expressed not only by words but by nonverbal behaviors such as facial expression and body posture as well. Thus, nonverbal behavior analysis is used as a key to automatic understanding of social interactions. This paper presents not only a survey of the related literature and the main concepts underlying SSP, but also an illustrative example of how such concepts are applied to the analysis of conflicts in competitive discussions. 1.
Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods. Networks and Heterogeneous
, 2011
"... Abstract. Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors th ..."
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Cited by 11 (1 self)
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Abstract. Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques. In this paper we present a methodological framework for the machine recog-nition of crowd behavior from on-body sensors, such as those in mobile phones. The recognition of crowd behaviors opens the way to the acquisition of large-scale datasets for the analysis and understanding of crowd dynamics. It has also practical safety applications by providing improved crowd situational aware-ness in cases of emergency. The framework comprises: behavioral recognition with the user’s mobile device, pairwise analyses of the activity relatedness of two users, and graph clustering in order to uncover globally, which users participate in a given crowd behavior. We illustrate this framework for the identification of groups of per-sons walking, using empirically collected data. We discuss the challenges and research avenues for theoretical and applied mathematics arising from the mobile sensing of crowd behaviors. 1. Introduction. Nowadays
Social Signal Processing: The Research Agenda
"... Abstract. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ignoring a crucial range of abilities that matter immen ..."
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Cited by 1 (0 self)
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Abstract. The exploration of how we react to the world and interact with it and each other remains one of the greatest scientific challenges. Latest research trends in cognitive sciences argue that our common view of intelligence is too narrow, ignoring a crucial range of abilities that matter immensely for how people do in life. This range of abilities is called social intelligence and includes the ability to express and recognise social signals produced during social interactions like agreement, politeness, empathy, friendliness, conflict, etc., coupled with the ability to manage them in order to get along well with others while winning their cooperation. Social Signal Processing (SSP) is the new research domain that aims at understanding and modelling social interactions (human-science goals), and at providing computers with similar abilities in human-computer interaction scenarios (technological goals). SSP is in its infancy, and the journey towards artificial social intelligence and socially-aware computing is still long. This research agenda is a twofold, a discussion about how the field is understood by people who are currently active in it and a discussion about issues that the researchers in this formative field face.
Communication: Vocal Behavior in Social and Affective Phenomena
, 2012
"... Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalization ..."
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Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communication”. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future.
Towards a Technology of Nonverbal Communication: Vocal Behavior in Social and Affective Phenomena
"... Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalization ..."
Abstract
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Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communication”. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future.
