Dominance Detection in Meetings Using Easily Obtainable Features

Rienks, Rutger; Heylen, Dirk

doi:10.1007/11677482_7

Cited by 79 publications

(116 citation statements)

References 8 publications

(5 reference statements)

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“…Research efforts targeted a wide spectrum of problems, including conflict detection [28], communication dynamics [7,25], mimicry measurement [10], early detection of developmental and cognitive diseases [37], role recognition [38], prediction of negotiation outcomes [9], videosurveillance [4,5,6,8], etc. Furthermore, several works were dedicated to the automatic prediction of traits likely to be relevant in a teaching context like, in particular, personality [21,23,30] and dominance [13,27,34,35].…”

Section: Computingmentioning

confidence: 99%

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Predicting online lecture ratings based on gesturing and vocal behavior

Cheng

Salamin

Salvagnini

et al. 2014

J Multimodal User Interfaces

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Nonverbal behavior plays an important role in any human-human interaction. Teaching -a inherently social activity -is not an exception. So far, the effect of nonverbal behavioral cues accompanying lecture delivery was investigated in the case of traditional ex-cathedra lectures, where students and teachers are co-located. However, it is becoming increasingly more frequent to watch lectures online and, in this new type of setting, it is still unclear what the effect of nonverbal communication is. This article tries to address the problem and proposes experiments performed over the lectures of a popular web repository ("Videolectures"). The results show that automatically extracted nonverbal behavioral cues (prosody, voice quality and gesturing activity) predict the ratings that "Videolectures" users assign to the presentations.

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Section: Computingmentioning

confidence: 99%

“…In [34,35], speaking activity (speaking time, number of turns, interruptions, etc.) and SVMs have been used to predict whether people are low, normal or high in dominance.…”

Section: Computingmentioning

confidence: 99%

Predicting online lecture ratings based on gesturing and vocal behavior

Cheng

Salamin

Salvagnini

et al. 2014

J Multimodal User Interfaces

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“…Recent work in AI explores methods for the automatic detection of other types of pragmatic variation in text and conversation, such as emotion (Oudeyer, 2002;Liscombe, Venditti, & Hirschberg, 2003), deception (Newman, Pennebaker, Berry, & Richards, 2003;Enos, Benus, Cautin, Graciarena, Hirschberg, & Shriberg, 2006;Graciarena, Shriberg, Stolcke, Enos, Hirschberg, & Kajarekar, 2006;Hirschberg, Benus, Brenier, Enos, Friedman, Gilman, Girand, Graciarena, Kathol, Michaelis, Pellom, Shriberg, & Stolcke, 2005), speaker charisma (Rosenberg & Hirschberg, 2005), mood (Mishne, 2005), dominance in meetings (Rienks & Heylen, 2006), point of view or subjectivity (Wilson, Wiebe, & Hwa, 2004;Wiebe, Wilson, Bruce, Bell, & Martin, 2004;Wiebe & Riloff, 2005;Stoyanov, Cardie, & Wiebe, 2005;Somasundaran, Ruppenhofer, & Wiebe, 2007), and sentiment or opinion (Turney, 2002;Pang & Lee, 2005;Popescu & Etzioni, 2005;Breck, Choi, & Cardie, 2007). In contrast with these pragmatic phenomena, which may be relatively contextualised or short-lived, personality is usually considered to be a longer term, more stable, aspect of individuals (Scherer, 2003).…”

Section: Introductionmentioning

confidence: 99%

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Mairesse¹,

Walker²,

Mehl³

et al. 2007

jair

746

583

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It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using selfreports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.

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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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Dominance Detection in Meetings Using Easily Obtainable Features

Abstract: We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.

Cited by 79 publications

References 8 publications

Predicting online lecture ratings based on gesturing and vocal behavior

Predicting online lecture ratings based on gesturing and vocal behavior

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Real-Time Feedback System for Monitoring and Facilitating Discussions

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