Results 1 - 10
of
33
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- ..."
Abstract
-
Cited by 153 (32 self)
- Add to MetaCart
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 ..."
Abstract
-
Cited by 35 (7 self)
- Add to MetaCart
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- ..."
Abstract
-
Cited by 27 (7 self)
- Add to MetaCart
(Show Context)
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 ..."
Abstract
-
Cited by 14 (2 self)
- Add to MetaCart
(Show Context)
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.
Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space
- In: IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW
, 2010
"... Socially intelligent surveillance and monitoring: ..."
Abstract
-
Cited by 14 (11 self)
- Add to MetaCart
(Show Context)
Socially intelligent surveillance and monitoring:
Towards computational proxemics: Inferring social relations from interpersonal distances
- in: Proc. of IEEE International Conference on Social Computing
, 2011
"... Abstract—This paper proposes a study corroborated by preliminary experiments on the inference of social relations based on the analysis of interpersonal distances, measured with onobtrusive computer vision techniques. The experiments have been performed over 13 individuals involved in casual standin ..."
Abstract
-
Cited by 7 (1 self)
- Add to MetaCart
(Show Context)
Abstract—This paper proposes a study corroborated by preliminary experiments on the inference of social relations based on the analysis of interpersonal distances, measured with onobtrusive computer vision techniques. The experiments have been performed over 13 individuals involved in casual standing conversations and the results show that people tend to get closer when their relation is more intimate. In other words, social and physical distances tend to match one another. In this respect, the results match the findings of proxemics, the discipline studying the social and affective meaning of space use and organization in social gatherings. The match between results and expectations of proxemics is observed also when changing one of the most important contextual factors in this type of scenarios, namely the amount of space available to the interactants. I.
NON-VERBAL BEHAVIORS OF EFFECTIVE TEACHERS OF AT-RISK AFRICAN-AMERICAN MALE MIDDLE SCHOOL STUDENTS Frederick Douglas Boyd, Sr.
"... Students in school districts throughout the United States are administered standardized tests in an effort to assess achievement. These annual "academic rites of passage" serve as measures of accountability to the citizenry of every locality served. Many at-risk African-American males scor ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
Students in school districts throughout the United States are administered standardized tests in an effort to assess achievement. These annual "academic rites of passage" serve as measures of accountability to the citizenry of every locality served. Many at-risk African-American males score in the lower two quartiles on these tests. Remediation efforts have not significantly raised the achievement of these students. However, there are teachers who are effective with these students. They use both verbal and non-verbal behaviors that facilitate learning. This study was designed to answer the question: What non-verbal behaviors are used by effective teachers of at-risk African-American male middle school students? Data were collected via teacher observations using the Non-verbal Behavior Teacher Observation Form, an instrument developed to record nonverbal behaviors of teachers. The instrument consists of thirteen behaviors that cover seven non-verbal domains. Four teachers were observed three times each for thirty minutes and two teachers were observed one time. The researcher selected a different at-risk male student each observation resulting in a total of fourteen teacher observations and their interactions with fourteen at-risk male students. Descriptive statistics were used to identify most frequently and least frequently used nonverbal behaviors. When effective teachers in this study interacted with the at-risk African-American male middle school students, they frequently were in close proximity, changed their voice inflections, established eye contact, invaded students' territories (were within two feet), and gestured to students. The results of this study may be used as a vehicle or catalyst for the implementation of a school or district-wide training program fo...
Enhancing Acquisition of Intercultural Nonverbal Competence: Thai English as a Foreign Language Learners and the Use of Contemporary English Language Films
, 2008
"... ii ..."
Verbal and Nonverbal Communication: Distinguishing Symbolic, Spontaneous, and Pseudo-Spontaneous Nonverbal Behaviour
- Journal of Communication
, 2002
"... Verbal and nonverbal communication are seen in terms of interacting streams of spontaneous and symbolic communication, and posed “pseudo-spontaneous ” dis-plays. Spontaneous communication is defined as the nonintentional communica-tion of motivational-emotional states based upon biologically shared ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
Verbal and nonverbal communication are seen in terms of interacting streams of spontaneous and symbolic communication, and posed “pseudo-spontaneous ” dis-plays. Spontaneous communication is defined as the nonintentional communica-tion of motivational-emotional states based upon biologically shared nonpropositional signal systems, with information transmitted via displays. Sym-bolic communication is the intentional communication, using learned, socially shared signal systems, of propositional information transmitted via symbols. Pseudo-spontaneous communication involves the intentional and strategic manipulation of displays. An original meta-analysis demonstrates that, like verbal symbolic com-munication, nonverbal analogic (pantomimic) communication is related to left hemisphere cerebral processing. In contrast, spontaneous communication is re-lated to the right hemisphere. A general theory of communication should account for the natural biologically based aspects of communication as well as its learned and symbolically structured aspects. Further, such a general theory should include a feedback process—expla-
Probabilistic Prediction of Student Affect from Hand Gestures
"... Abstract — Affective information is vital for effective human-tohuman communication. Likewise, human-to-computer communication could be potentiated by an “affective barometer ” able to infer human affect using a machine vision system. For instance, during a classroom lecture, an affective barometer ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract — Affective information is vital for effective human-tohuman communication. Likewise, human-to-computer communication could be potentiated by an “affective barometer ” able to infer human affect using a machine vision system. For instance, during a classroom lecture, an affective barometer might provide useful feedback that a real or virtual instructor could use to improve pedagogical strategies. In this paper, we explore the feasibility of using students ’ unintentional hand gestures during a classroom lecture to predict their affective state. We propose a maximum a posteriori classifier based on a simple Bayesian network model. We then evaluate the classifier’s ability to predict one of four affective states from five hand gestures observed in video recordings of a classroom lecture. Using four-fold cross validation, we find that the model’s generalization accuracy is 100 % over cases where the student reported an affective state, and 79.4 % when we include cases where the student reported no affective state. The experiment demonstrates that there is a strong relationship between human affect and visually observable gestures. Future work will explore the applicability of these results in practical applications. Index Terms — Behavior recognition, Intelligent tutoring systems, Human-computer interaction, Probabilistic affect prediction,
