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41
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.
Modeling dominance in group conversations using non-verbal activity cues
- IDIAP Research Report
, 2007
"... Abstract — Dominance- a behavioral expression of power- is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audio-visual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in soci ..."
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Cited by 48 (21 self)
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Abstract — Dominance- a behavioral expression of power- is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audio-visual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audio-visual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date. Index Terms — Group Meetings, dominance modeling, nonverbal communication, audio-visual activity cues
Using the influence model to recognize functional roles in meetings
- IN ICMI
, 2007
"... In this paper, an influence model is used to recognize functional roles played during meetings. Previous works on the same corpus demonstrated a high recognition accuracy using SVMs with RBF kernels. In this paper, we discuss the problems of that approach, mainly over-fitting, the curse of dimension ..."
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Cited by 42 (10 self)
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In this paper, an influence model is used to recognize functional roles played during meetings. Previous works on the same corpus demonstrated a high recognition accuracy using SVMs with RBF kernels. In this paper, we discuss the problems of that approach, mainly over-fitting, the curse of dimensionality and the inability to generalize to different group configurations. We present results obtained with an influence modeling method that avoid these problems and ensures both greater robustness and generalization capability.
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.
Predicting Two Facets of Social Verticality in Meetings from Five-Minute Time Slices and Nonverbal Cues
"... This paper addresses the automatic estimation of two aspects of social verticality (status and dominance) in small-group meetings using nonverbal cues. The correlation of nonverbal behavior with these social constructs have been extensively documented in social psychology, but their value for comput ..."
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Cited by 18 (6 self)
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This paper addresses the automatic estimation of two aspects of social verticality (status and dominance) in small-group meetings using nonverbal cues. The correlation of nonverbal behavior with these social constructs have been extensively documented in social psychology, but their value for computational models is, in many cases, still unknown. We present a systematic study of automatically extracted cues- including vocalic, visual activity, and visual attention cues- and investigate their relative effectiveness to predict both the most-dominant person and the high-status project manager from relative short observations. We use five hours of task-oriented meeting data with natural behavior for our experiments. Our work suggests that, although dominance and role-based status are related concepts, they are not equivalent and are thus not equally explained by the same nonverbal cues. Furthermore, the best cues can correctly predict the person with highest dominance or role-based status with an accuracy of 70 % approximately.
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.
A Nonverbal Behavior Approach to Identify Emergent Leaders in Small Groups
"... Abstract—Identifying emergent leaders in organizations is a key issue in organizational behavioral research, and a new problem in social computing. This paper presents an analysis on how an emergent leader is perceived in newly formed, small groups, and then tackles the task of automatically inferri ..."
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Cited by 9 (4 self)
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Abstract—Identifying emergent leaders in organizations is a key issue in organizational behavioral research, and a new problem in social computing. This paper presents an analysis on how an emergent leader is perceived in newly formed, small groups, and then tackles the task of automatically inferring emergent leaders, using a variety of communicative nonverbal cues extracted from audio and video channels. The inference task uses rule-based and collective classification approaches with the combination of acoustic and visual features extracted from a new small group corpus specifically collected to analyze the emergent leadership phenomenon. Our results show that the emergent leader is perceived by his/her peers as an active and dominant person; that visual information augments acoustic information; and that adding relational information to the nonverbal cues improves the inference of each participant’s leadership rankings in the group. Index Terms—Emergent Leadership, Nonverbal behavior I.
