Look at Who’s Talking: Voice Activity Detection by Automated Gesture Analysis

Cristani, Marco; Pesarin, Anna; Vinciarelli, Alessandro; Crocco, Marco; Murino, Vittorio

doi:10.1007/978-3-642-31479-7_14

Cited by 20 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…However, audio-related cues can not be extracted reliably in airports. Cristani et al (2012) use body behavior and gestures to classify a video of four participants having a conversation into intervals of speech or non-speech. The method achieves 72% accuracy, but the setting is static.…”

Section: Spokesperson Detectionmentioning

confidence: 99%

SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

Triebel

Arras

Alami

et al. 2016

Springer Tracts in Advanced Robotics

202

124

View full text Add to dashboard Cite

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

show abstract

“…In [49], gesturing is used to infer who is talking when in a surveillance scenario, realizing through statistical analysis a simple form of diarization (detection of who speaks when, [50]). Actually, cognitive scientists showed that speech and gestures are so tightly intertwined that every important investigation of language has taken gestures into account [51].…”

Section: Gesture and Posturementioning

confidence: 99%

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

et al. 2013

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Look at Who’s Talking: Voice Activity Detection by Automated Gesture Analysis

Cited by 20 publications

References 14 publications

Predicting online lecture ratings based on gesturing and vocal behavior

Predicting online lecture ratings based on gesturing and vocal behavior

SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

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

Contact Info

Product

Resources

About