Multimodal recognition of personality traits in social interactions

Pianesi, Fabio; Mana, Nadia; Cappelletti, Alessandro; Lepri, Bruno; Zancanaro, Massimo

doi:10.1145/1452392.1452404

Cited by 153 publications

(121 citation statements)

References 19 publications

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“…Jayagopi et al estimate dominant behavior by computing speaking turns-based features (like speaking time, turns, successful interruptions) along with motion turns and learning supervised models using Support Vector Machines (SVM) on meetings from the AMI (Augmented Multiparty Interaction) corpus [4,23]. Pianesi et al [39,40] estimate personality traits, specifically extraversion (sociable, assertive, playful) versus intraversion (aloof, reserved, shy) using Support Vector Regression and applied to sequences of the MS (Mission Survival) Corpus. Using an influence model, Dong et al [9] estimated functional roles in meetings related to tasks and socio-emotional roles on the MS Corpus.…”

Section: Human Behavior Analysis Using Infrastructurebased Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

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Recognizing the conversational context in which group interactions unfold has applications in machines that support collaborative work and perform automatic social inference using contextual knowledge. This paper addresses the task of discriminating one conversational context from another, specifically brainstorming from decision-making interactions, using easily computable nonverbal behavioral cues. Privacy-sensitive mobile sociometers are used to record the interaction data. We hypothesize that the difference in the conversational dynamics between brainstorming and decision-making discussions is significant and measurable using speaking activity-based nonverbal cues. We characterize the communication patterns of the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. The results on our interaction data set show that the floor-occupation patterns in a brainstorming interaction are different from a decision-making interaction, and our method can obtain a classification accuracy as high as 87.5%.

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Section: Human Behavior Analysis Using Infrastructurebased Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

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“…Recent studies in this domain such as [5], [6], [7], [8] have used the traits outlined by the Big Five (BF) [9] personality model, widely used in psychology, to assess human personality. The BF model encapsulate different human personality traits along five dimensions:(1) Extraversion describes how outgoing and energetic one is; (2) Agreeableness describes one's orientation towards other people and one's tendency for compliance; (3) Conscientiousness describes one's tendency to favor structure, order and a planned behavior; (4) Neuroticism describes one's tendency to negative emotions such as anxiety, hostility, depression or anger; and (5) Openness to experience describes one's tendency to changing experience, adventure, ideas etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic Prediction of Perceived Traits Using Visual Cues under Varied Situational Context

Joshi

Güneş

Goecke

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Automatic assessment of human personality traits is a non-trivial problem, especially when perception is marked over a fairly short duration of time. In this study, thin slices of behavioral data are analyzed. Perceived physical and behavioral traits are assessed by external observers (raters). Along with the big-five personality trait model, four new traits are introduced and assessed in this work. The relationship between various traits is investigated to obtain a better understanding of observer perception and assessment. Perception change is also considered when participants interact with several virtual characters each with a distinct emotional style. Encapsulating these observations and analysis, an automated system is proposed by firstly computing low level visual features. Using these features a separate model is trained for each trait and performance is evaluated. Further, a weighted model based on rater credibility is proposed to address observer biases. Experimental results indicate that a weighted model show major improvement for automatic prediction of perceived physical and behavioral traits.

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“…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]. In such studies, various statistics of the conversation are extracted, e.g., natural turns, turn duration, speaking percentage, interruptions and failed interruptions.…”

Section: Introductionmentioning

confidence: 99%

“…However, in none of those studies [6][7][8][9][10][11], social interactions are analysed in real-time, instead various corpora of audio and video recordings are analysed offline [12]. Many corpora of audio and video signals are available related to small-group interactions (see [13] for a survey).…”

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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Multimodal recognition of personality traits in social interactions

Cited by 153 publications

References 19 publications

Privacy-sensitive recognition of group conversational context with sociometers

Privacy-sensitive recognition of group conversational context with sociometers

Automatic Prediction of Perceived Traits Using Visual Cues under Varied Situational Context

Real-Time Feedback System for Monitoring and Facilitating Discussions

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