Automatic nonverbal analysis of social interaction in small groups: A review

Gática-Pérez, Daniel

doi:10.1016/j.imavis.2009.01.004

Cited by 280 publications

(205 citation statements)

References 103 publications

(166 reference statements)

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“…Actually automatic analysis treats many issues [7], among which can be mentioned: addressing, turn taking, activity recognition, roles, degree of interest or engagement, state of mind (e.g. neutral, curious, confused, amused) and dominance.…”

Section: Related Workmentioning

confidence: 99%

“…First, speaker diarization is applied to list turns; second, acoustic features are extracted from turns and finally, features vectors are mapped into a sequence of roles. More complete reviews on issues and models related to nonverbal analysis of social interaction can be found in [11] [7]. In the context of multimodal behavior generation, several platforms have been proposed for humanoid robots and virtual agents.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Social Behavior Modeling Based on Incremental Discrete Hidden Markov Models

Mihoub

Bailly

Wolf

2013

Human Behavior Understanding

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Social Behavior Modeling Based on Incremental Discrete Hidden Markov Models

Mihoub

Bailly

Wolf

2013

Human Behavior Understanding

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“…The first is that computing domains aimed at understanding social and affective behavior have focused on face-to-face small group interactions [26,66]. These have been the subject of major attention because, one one hand, they represent the most common and primordial form of human-human interaction and, on the other hand, they involve those social and emotional phenomena that most affect our life like conflict, exclusion, affiliation, roles, dominance, personality, performance, etc.…”

Section: Socially and Emotionally Aware Technologiesmentioning

confidence: 99%

Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space

Cristani

Murino

Vinciarelli

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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In general terms, surveillance and monitoring technologies aim at understanding what people do in a given environment, whether this means to ensure the safety of workers on the factory floor, to detect crimes occurring in indoor or outdoor settings, or to monitor the flow of large crowds through public spaces. However, surveillance and monitoring technologies rarely consider that they analyze human behavior, a phenomenon subject to principles and laws rigorous enough to produce stable and predictable patterns corresponding to social, affective, and psychological phenomena. On the other hand, these phenomena are the subject of other computing domains, in particular Social Signal Processing and Affective Computing, that typically neglect scenarios relevant to surveillance and monitoring technologies, especially when it comes to social and affective dimensions of space in human activities. The goal of this paper is to show that the investigation of the overlapping area between surveillance and monitoring on one side, and Social Signal Processing and Affective Computing on the other side can bring significant progress in both domains and open a number of interesting research perspectives.

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“…Both approaches are time-consuming and unavoidably subjective. Recent advances in recording equipment [5] and signal processing may ultimately enable automated and realtime analysis of talking mannerisms and social interactions at large, yielding more objective results. Several studies in that direction have been conducted in recent years, to deduce individual characteristics like dominance status [6][7], emerging leadership [8] and other personality related traits [9][10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Feedback System for Monitoring and Facilitating Discussions

Sarda

Constable

Dauwels

et al. 2013

Natural Interaction With Robots, Knowbots and Smartphones

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Abstract-In this paper we present a system that provides realtime feedback about an ongoing discussion. Various speech statistics, such as speaking length, speaker turns, and speaking turn duration, are computed and displayed in real-time. In social monitoring, such statistics have been used to interpret and deduce talking mannerisms of people, and gain insights on human social characteristics and behaviour. However, such analysis is usually conducted in an offline fashion, after the discussion has ended. In contrast, our system analyses the speakers and provides feedback to the speakers in real-time during the discussion, which is a novel approach with plenty of potential applications. The proposed system consists of portable, easy to use equipment for recording the conversations. A user friendly graphical user interface displays statistics about the ongoing discussion. Customized individual feedback to participants during conversation can be provided. Such close-loop design may help individuals to contribute effectively in the group discussion, potentially leading to more productive and perhaps shorter meetings. Here we present preliminary results on two-people face to face discussion. In the longer term, our system may prove to be useful, e.g., for coaching purposes, and for facilitating business meetings.

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Automatic nonverbal analysis of social interaction in small groups: A review

Cited by 280 publications

References 103 publications

Social Behavior Modeling Based on Incremental Discrete Hidden Markov Models

Social Behavior Modeling Based on Incremental Discrete Hidden Markov Models

Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space

Real-Time Feedback System for Monitoring and Facilitating Discussions

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