Analysis of Nonverbal Involvement in Dyadic Interactions

Altmann, Uwe; Hermkes, Rico; Alisch, Lutz-Michael

doi:10.1007/978-3-540-76442-7_4

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…In particular, nonverbal communication contributes to human communication in a variety of ways and is an important factor in social interaction [ 1 , 2 , 3 ]. Nonverbal responses in human communication are known to be immediate and honest [ 4 ]. Among nonverbal behaviors that contribute to human communication, nodding the head plays an important role as a form of feedback in human communication [ 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Nonverbal Synchronization through Phase Difference in Human Communication

et al. 2015

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Nonverbal communication is an important factor in human communication, and body movement synchronization in particular is an important part of nonverbal communication. Some researchers have analyzed body movement synchronization by focusing on changes in the amplitude of body movements. However, the definition of “body movement synchronization” is still unclear. From a theoretical viewpoint, phase difference is the most important factor in synchronization analysis. Therefore, there is a need to measure the synchronization of body movements using phase difference. The purpose of this study was to provide a quantitative definition of the phase difference distribution for detecting body movement synchronization in human communication. The phase difference distribution was characterized using four statistical measurements: density, mean phase difference, standard deviation (SD) and kurtosis. To confirm the effectiveness of our definition, we applied it to human communication in which the roles of speaker and listener were defined. Specifically, we examined the difference in the phase difference distribution between two different communication situations: face-to-face communication with visual interaction and remote communication with unidirectional visual perception. Participant pairs performed a task supposing lecture in the face-to-face communication condition and in the remote communication condition via television. Throughout the lecture task, we extracted a set of phase differences from the time-series data of the acceleration norm of head nodding motions between two participants. Statistical analyses of the phase difference distribution revealed the characteristics of head nodding synchronization. Although the mean phase differences in synchronized head nods did not differ significantly between the conditions, there were significant differences in the densities, the SDs and the kurtoses of the phase difference distributions of synchronized head nods. These results show the difference in nonverbal synchronization between different communication types. Our study indicates that the phase difference distribution is useful in detecting nonverbal synchronization in various human communication situations.

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Section: Introductionmentioning

confidence: 99%

Detection of Nonverbal Synchronization through Phase Difference in Human Communication

et al. 2015

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“…Works were done in the framework of varied physiological and psychological states of the clients, and in varied ages of clients, from children to adults. Within the realm of related works on prosodic characteristics of in-session discourse are, inter alia, [10], who studied nonverbal features in the psychotherapeutic process in order to find interactional indicators which predict psychotherapy to be successful or not. An example of acoustical analysis of therapy session's recordings can be found in [11].…”

Section: Related Workmentioning

confidence: 99%

In search of the role's footprints in client-therapist dialogues

Lerner¹,

Silber-Varod²,

Batista³

et al. 2016

Speech Prosody 2016

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The goal of this research is to identify speaker's role via machine learning of broad acoustic parameters, in order to understand how an occupation, or a role, affects voice characteristics. The examined corpus consists of recordings taken under the same psychological paradigm (Process Work). Four interns were involved in four genuine client-therapist treatment sessions, where each individual had to train her therapeutic skills on her colleague that, in her turn, participated as a client. This uniform setting provided a unique opportunity to examine how role affects speaker's prosody. By a collection of machine learning algorithms, we tested automatic classification of the role across sessions. Results based on the acoustic properties show high classification rates, suggesting that there are discriminative acoustic features of speaker's role, as either a therapist or a client.

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“…), eye gestures (threatened gaze, inquisitive gaze, etc.) are some of the parameters that are studied closely in dyadic understanding of human bilateral communication [8]. Enriching SSA in dyadic communication thus focuses on appropriate extraction and delivery of communicator's face, body and voice based behaviors to a remote participant or to a person who is disabled.…”

Section: Ssa In Dyadic Interactionsmentioning

confidence: 99%

Enriching social situational awareness in remote interactions

Krishna

Balasubramanian

Panchanathan

2010

Proceedings of the 18th ACM International Conference on Multimedia

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In this paper we present a new perspective into developing technologies for enriching social presence among remote interaction partners. Inspired by the abilities and limitations faced by people who are disabled during their everyday social interactions, we propose novel portable and wearable technologies that could potentially enrich remote interactions even in audio and video deprived settings. We describe the important challenges faced by people who are disabled during everyday dyadic and group social interactions and correlate them to the challenges faced by participants in remote interactions. With a case study of visually impaired individuals, we demonstrate how assistive technologies developed for social assistance of people who are disabled can help in increasing the social situational awareness, and hence social presence, of remote interaction partners.

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Analysis of Nonverbal Involvement in Dyadic Interactions

Cited by 9 publications

References 18 publications

Detection of Nonverbal Synchronization through Phase Difference in Human Communication

Detection of Nonverbal Synchronization through Phase Difference in Human Communication

In search of the role's footprints in client-therapist dialogues

Enriching social situational awareness in remote interactions

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