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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.
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.
Recent Developments in Social Signal Processing
"... Abstract—Social signal processing has the ambitious goal of bridging the social intelligence gap between computers and humans. Nowadays, computers are not only the new interaction partners of humans, but also a privileged interaction medium for social exchange between humans. Consequently, enhancing ..."
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Cited by 4 (3 self)
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Abstract—Social signal processing has the ambitious goal of bridging the social intelligence gap between computers and humans. Nowadays, computers are not only the new interaction partners of humans, but also a privileged interaction medium for social exchange between humans. Consequently, enhancing machine abilities to interpret and reproduce social signals is a crucial requirement for improving computer-mediated communication and interaction. Furthermore, automated analysis of such signals creates a host of new applications and improvements to existing applications. The study of social signals benefits a wide range of domains, including human-computer interaction, interaction design, entertainment technology, ambient intelligence, healthcare, and psychology. This paper briefly introduces the field and surveys its latest developments. Index Terms—Behavioral science, human computer interaction, emotion recognition I.
An analysis of pca-based vocal entrainment measures in married couples’ affective spoken interactions
- In: Proceedings of the Interspeech
"... Abstract Entrainment has played a crucial role in analyzing marital couples interactions. In this work, we introduce a novel technique for quantifying vocal entrainment based on Principal Component Analysis (PCA). The entrainment measure, as we define in this work, is the amount of preserved variab ..."
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Cited by 2 (0 self)
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Abstract Entrainment has played a crucial role in analyzing marital couples interactions. In this work, we introduce a novel technique for quantifying vocal entrainment based on Principal Component Analysis (PCA). The entrainment measure, as we define in this work, is the amount of preserved variability of one interlocutor's speaking characteristic when projected onto representing space of the other's speaking characteristics. Our analysis on real couples interactions shows that when a spouse is rated as having positive emotion, he/she has a higher value of vocal entrainment compared when rated as having negative emotion. We further performed various statistical analyses on the strength and the directionality of vocal entrainment under different affective interaction conditions to bring quantitative insights into the entrainment phenomenon. These analyses along with a baseline prediction model demonstrate the validity and utility of the proposed PCA-based vocal entrainment measure.
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.
Language Style Matching in Writing: Synchrony in Essays, Correspondence, and Poetry
"... Each relationship has its own personality. Almost immediately after a social interaction begins, verbal and nonverbal behaviors become synchronized. Even in asocial contexts, individuals tend to produce utterances that match the grammatical structure of sentences they have recently heard or read. Th ..."
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Each relationship has its own personality. Almost immediately after a social interaction begins, verbal and nonverbal behaviors become synchronized. Even in asocial contexts, individuals tend to produce utterances that match the grammatical structure of sentences they have recently heard or read. Three projects explore language style matching (LSM) in everyday writing tasks and professional writing. LSM is the relative use of 9 function word categories (e.g., articles, personal pronouns) between any 2 texts. In the first project, 2 samples totaling 1,744 college students answered 4 essay questions written in very different styles. Students automatically matched the language style of the target questions. Overall, the LSM metric was internally consistent and reliable across writing tasks. Women, participants of higher socioeconomic status, and students who earned higher test grades matched with targets more than others did. In the second project, 74 participants completed cliffhanger excerpts from popular fiction. Judges ’ ratings of excerpt–response similarity were related to content matching but not function word matching, as indexed by LSM. Further, participants were not able to intentionally increase style or content matching. In the final project, an archival study tracked the professional writing and personal correspondence of 3 pairs of famous writers across their relationships. Language matching in poetry and letters reflected fluctuations in the relationships of 3 couples:
ORI GIN AL PA PER Measuring the Dynamics of Interactional Synchrony
, 2012
"... Abstract Past research has revealed that natural social interactions contain interactional synchrony. The present study describes new methods for measuring interactional syn-chrony in natural interactions and evaluates whether the behavioral synchronization involved in social interactions is similar ..."
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Abstract Past research has revealed that natural social interactions contain interactional synchrony. The present study describes new methods for measuring interactional syn-chrony in natural interactions and evaluates whether the behavioral synchronization involved in social interactions is similar to dynamical synchronization found generically in nature. Two methodologies, a rater-coding method and a computational video image method, were used to provide time series representations of the movements of the co-actors as they enacted a series of jokes (i.e., knock–knock jokes). Cross-spectral and relative phase analyses of these time series revealed that speakers ’ and listeners ’ movements contained rhythms that were not only correlated in time but also exhibited phase syn-chronization. These results suggest that computational advances in video and time series analysis have greatly enhanced our ability to measure interactional synchrony in natural interactions. Moreover, the dynamical synchronization in these natural interactions is commensurate with that found in more stereotyped tasks, suggesting that similar organi-zational processes constrain bodily activity in natural social interactions and, hence, have implications for the understanding of joint action generally.
