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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.
Role recognition for meeting participants: an approach based on lexical information and Social Network Analysis
- in Proceedings of the ACM International Conference on Multimedia, 2008
"... This paper presents experiments on the automatic recognition of roles in meetings. The proposed approach combines two sources of information: the lexical choices made by people playing different roles on one hand, and the Social Networks describing the interactions between the meeting participants o ..."
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Cited by 30 (13 self)
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This paper presents experiments on the automatic recognition of roles in meetings. The proposed approach combines two sources of information: the lexical choices made by people playing different roles on one hand, and the Social Networks describing the interactions between the meeting participants on the other hand. Both sources lead to role recognition results significantly higher than chance when used separately, but the best results are obtained with their combination. Preliminary experiments obtained over a corpus of 138 meeting recordings (over 45 hours of material) show that around 70 % of the time is labeled correctly in terms of role.
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.
Role Recognition in Multiparty Recordings using Social Affiliation Networks and Discrete Distributions
- In Proceedings of the ACM International Conference on Multimodal Interfaces
, 2008
"... This paper presents an approach for the recognition of roles in multiparty recordings. The approach includes two major stages: extraction of Social Affiliation Networks (speaker diarization and representation of people in terms of their social interactions), and role recognition (application of disc ..."
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Cited by 20 (6 self)
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This paper presents an approach for the recognition of roles in multiparty recordings. The approach includes two major stages: extraction of Social Affiliation Networks (speaker diarization and representation of people in terms of their social interactions), and role recognition (application of discrete probability distributions to map people into roles). The experiments are performed over several corpora, including broadcast data and meeting recordings, for a total of roughly 90 hours of material. The results are satisfactory for the broadcast data (around 80 percent of the data time correctly labeled in terms of role), while they still must be improved in the case of the meeting recordings (around 45 percent of the data time correctly labeled). In both cases, the approach outperforms significantly chance.
Automatic Role Recognition in Multiparty Recordings: Using Social Affiliation Networks for Feature Extraction
"... Abstract—Automatic analysis of social interactions attracts increasing attention in the multimedia community. This paper considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automati ..."
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Cited by 18 (6 self)
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Abstract—Automatic analysis of social interactions attracts increasing attention in the multimedia community. This paper considers one of the most important aspects of the problem, namely the roles played by individuals interacting in different settings. In particular, this work proposes an automatic approach for the recognition of roles in both production environment contexts (e.g., news and talk-shows) and spontaneous situations (e.g., meetings). The experiments are performed over roughly 90 hours of material (one of the largest databases used for role recognition in the literature) and show that the recognition effectiveness depends on how much the roles influence the behavior of people. Furthermore, this work proposes the first approach for modeling mutual dependences between roles and assesses its effect on role recognition performance.
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.
Automatic Role Recognition in Multiparty Recordings Using Social Networks and Probabilistic Sequential Models
"... The automatic analysis of social interactions is attracting significant interest in the multimedia community. This work addresses one of the most important aspects of the problem, namely the recognition of roles in social exchanges. The proposed approach is based on Social Network Analysis, for the ..."
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Cited by 5 (3 self)
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The automatic analysis of social interactions is attracting significant interest in the multimedia community. This work addresses one of the most important aspects of the problem, namely the recognition of roles in social exchanges. The proposed approach is based on Social Network Analysis, for the representation of individuals in terms of their interactions with others, and probabilistic sequential models, for the recognition of role sequences underlying the sequence of speakers in conversations. The experiments are performed over different kinds of data (around 90 hours of broadcast data and meetings), and show that the performance depends on how formal the roles are, i.e. on how much they constrain people behavior.
Automatic Role Recognition in Multiparty Conversations: An Approach Based on Turn Organization
- Prosody, and Conditional Random Fields,” IEEE Transactions on Multimedia
, 2012
"... Abstract—Roles are a key aspect of social interactions, as they contribute to the overall predictability of social behavior (a nec-essary requirement to deal effectively with the people around us), and they result in stable, possibly machine-detectable behavioral patterns (a key condition for the ap ..."
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Cited by 2 (0 self)
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Abstract—Roles are a key aspect of social interactions, as they contribute to the overall predictability of social behavior (a nec-essary requirement to deal effectively with the people around us), and they result in stable, possibly machine-detectable behavioral patterns (a key condition for the application of machine intelli-gence technologies). This paper proposes an approach for the au-tomatic recognition of roles in conversational broadcast data, in particular, news and talk shows. The approach makes use of be-havioral evidence extracted from speaker turns and applies con-ditional random fields to infer the roles played by different indi-viduals. The experiments are performed over a large amount of broadcast material (around 50 h), and the results show an accu-racy higher than 85%. Index Terms—Conditional random fields (CRFs), prosody, role recognition, turn organization. I.
COLLECTING DATA FOR SOCIALLY INTELLIGENT SURVEILLANCE ANDMONITORING APPROACHES: THE CASE OF CONFLICT IN COMPETITIVE CONVERSATIONS
"... One of the most recent trends in surveillance research is the application of socially aware approaches, i.e. approaches that integrate human sciences findings in order to better under-stand, model and predict the behaviour of people under ob-servation. One of the key requirements for the development ..."
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Cited by 2 (1 self)
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One of the most recent trends in surveillance research is the application of socially aware approaches, i.e. approaches that integrate human sciences findings in order to better under-stand, model and predict the behaviour of people under ob-servation. One of the key requirements for the development of such approaches is the collection of corpora that provide sufficient and reliable information about social phenomena of interest. However, the computing community still pays rela-tively little attention to the application of methodologies suit-able for observational data collection, possibly inspired by human sciences experimental work. This paper tries to fill, at least partially, such a gap by providing an introduction to data collection techniques applied in nonverbal behaviour re-search. The collection of a corpus aimed at the study of con-flict in conversations is used as a case study and example. Index Terms — Social Signal Processing, Conflict, Non-verbal behaviour 1.
