A multimodal annotated corpus of consensus decision making meetings

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

doi:10.1007/s10579-007-9060-6

Cited by 52 publications

(54 citation statements)

References 18 publications

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“…The latter has been also investigated by means of experiments performed on meeting corpora such as the AMI Corpus [21] and the ICSI Corpus [23]. Other databases, albeit not annotated in terms of (dis)agreement or conflict, that contain multiple instances of the latter behaviors as well as other social behaviors (e.g., interest, politeness, mimicry, flirting), social signals (e.g., social dominance, engagement, hot-spots, acceptance, blame) and personality traits include [22,24,25,27], the Green Persuasive Dataset and the newly released SEWA Database. It is worth mentioning that the SEWA Database is the largest and the richest database of human conversational and emotional behaviour that has been released so far.…”

Section: Prior Workmentioning

confidence: 99%

The Conflict Escalation Resolution (CONFER) Database

Georgakis

Panagakis

Zafeiriou

et al. 2017

Image and Vision Computing

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Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audio-visual recordings of televised political debates where conflicts naturally arise, and b) reporting baseline experiments on audio-visual conflict intensity estimation. The database contains approximately 142 minutes of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject-and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict clas- * Corresponding author. E-mail address: christos.georgakis@imperial.ac.uk Preprint submitted to Image and Vision Computing December 20, 2016 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTsification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/.

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Section: Prior Workmentioning

confidence: 99%

The Conflict Escalation Resolution (CONFER) Database

Georgakis

Panagakis

Zafeiriou

et al. 2017

Image and Vision Computing

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“…Among the multimodal corpora in the literature, the closest to our work is the Mission Survival Corpus (MSC-1 and MSC-2) (Mana et al, 2007;Pianesi et al, 2007). The data comprises small groups performing the winter survival task.…”

Section: Related Workmentioning

confidence: 99%

Emergent leaders through looking and speaking: from audio-visual data to multimodal recognition

Sanchez-Cortes

Aran

Kumar

et al. 2012

J Multimodal User Interfaces

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In this paper we present a multimodal analysis of emergent leadership in small groups using audio-visual features and discuss our experience in designing and collecting a data corpus for this purpose. The ELEA AudioVisual Synchronized corpus (ELEA AVS) was collected using a light portable setup and contains recordings of small group meetings. The participants in each group performed the winter survival task and filled in questionnaires related to personality and several social concepts such as leadership and dominance. In addition, the corpus includes annotations on participants' performance in the survival task, and also annotations of social concepts from external viewers. Based on this corpus, we present the feasibility of predicting the emergent leader in small groups using automatically extracted audio and visual features, based on speaking turns and visual attention, and we focus specifically on multimodal features that make use of the looking at participants while speaking and looking at while not speaking measures. Our findings indicate that emergent leadership is related, but not equivalent, to dominance, and while multimodal features bring a moderate degree of effectiveness in inferring the leader, much simpler features extracted from the audio channel are found to give better performance.

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“…Social role annotation are obtained with the same guidelines as [9] (CHIL project) where annotators where provided with audio and video and could assign a mapping speaker-to-role at any time instant. The guidelines define a set of acts and behaviors that character- which a speaker holds the conversation floor.…”

Section: Data and Annotationmentioning

confidence: 99%

“…Beside formal roles, other coding schemes have been proposed in literature with the purpose of generalizing across any type of conversations and settings, for instance, the Socio-Emotional roles [9]. Social roles are inspired from Bales work [10] and characterize the relationships between group members and their roles "oriented towards the functioning of the group as a group".…”

Section: Introductionmentioning

confidence: 99%

Automatic speaker role labeling in AMI meetings: Recognition of formal and social roles

Sapru

Valente

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work aims at investigating the automatic recognition of speaker role in meeting conversations from the AMI corpus. Two types of roles are considered: formal roles, fixed over the meeting duration and recognized at recording level, and social roles related to the way participants interact between themselves, recognized at speaker turn level. Various structural, lexical and prosodic features as well as Dialog Act tags are exhaustively investigated and combined for this purpose. Results reveal an accuracy of 74% in recognizing the speakers formal roles and an accuracy of 66% (percentage of time) in correctly labeling the social roles. Feature analysis reveals that lexical features provide the higher performances in formal/functional role recognition while prosodic features provide the higher performances in social role recognition. Furthermore results reveal that social role recognition in case of rare roles in the corpus can be improved through the use of lexical and Dialog Act information combined over short time windows.

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A multimodal annotated corpus of consensus decision making meetings

Cited by 52 publications

References 18 publications

The Conflict Escalation Resolution (CONFER) Database

The Conflict Escalation Resolution (CONFER) Database

Emergent leaders through looking and speaking: from audio-visual data to multimodal recognition

Automatic speaker role labeling in AMI meetings: Recognition of formal and social roles

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