Generative modeling and classification of dialogs by a low-level turn-taking feature

Cristani, Marco; Pesarin, Anna; Drioli, Carlo; Tavano, Alessandro; Perina, Alessandro; Murino, Vittorio

doi:10.1016/j.patcog.2011.01.013

Cited by 18 publications

(17 citation statements)

References 45 publications

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“…In particular, domains like Affective Computing [31] and Social Signal Processing [43] adopted nonverbal behavioral cues as a physical, machine detectable evidence of emotional and social phenomena, respectively. 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%

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%

Predicting online lecture ratings based on gesturing and vocal behavior

Cheng

Salamin

Salvagnini

et al. 2014

J Multimodal User Interfaces

Self Cite

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“…SCPs are built on the duration of continuous slots of silence or speech [27]. The SCP extraction procedure assumes that whenever a process (i.e, silence or speech) changes its state, it causes a global transition that affects also the opposite process, inserting a novel auto-transition state (see the red dashed lines in Fig.…”

Section: Methodsmentioning

confidence: 99%

Automatic Conversational Scene Analysis in Children with Asperger Syndrome/High-Functioning Autism and Typically Developing Peers

et al. 2014

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Individuals with Asperger syndrome/High Functioning Autism fail to spontaneously attribute mental states to the self and others, a life-long phenotypic characteristic known as mindblindness. We hypothesized that mindblindness would affect the dynamics of conversational interaction. Using generative models, in particular Gaussian mixture models and observed influence models, conversations were coded as interacting Markov processes, operating on novel speech/silence patterns, termed Steady Conversational Periods (SCPs). SCPs assume that whenever an agent's process changes state (e.g., from silence to speech), it causes a general transition of the entire conversational process, forcing inter-actant synchronization. SCPs fed into observed influence models, which captured the conversational dynamics of children and adolescents with Asperger syndrome/High Functioning Autism, and age-matched typically developing participants. Analyzing the parameters of the models by means of discriminative classifiers, the dialogs of patients were successfully distinguished from those of control participants. We conclude that meaning-free speech/silence sequences, reflecting inter-actant synchronization, at least partially encode typical and atypical conversational dynamics. This suggests a direct influence of theory of mind abilities onto basic speech initiative behavior.

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“…More recently [74,75], a generative framework has been proposed aimed at classifying conversation intervals of variable length (from a few minutes to hours), considering the nature of the people involved within (children, adults) and the main mood.…”

Section: Vocal Behaviormentioning

confidence: 99%

Human behavior analysis in video surveillance: A Social Signal Processing perspective

et al. 2013

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The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.

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Generative modeling and classification of dialogs by a low-level turn-taking feature

Cited by 18 publications

References 45 publications

Predicting online lecture ratings based on gesturing and vocal behavior

Predicting online lecture ratings based on gesturing and vocal behavior

Automatic Conversational Scene Analysis in Children with Asperger Syndrome/High-Functioning Autism and Typically Developing Peers

Human behavior analysis in video surveillance: A Social Signal Processing perspective

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