Addressee Identification In Face-to-Face Meetings

We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers’ performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker’s gaze information. The classifiers show little gain from information about meeting context

A corpus for studying addressing behaviour in multi-party dialogues

This paper describes a multi-modal corpus of hand-annotated meeting dialogues that was designed for studying addressing behaviour in face-to-face conversations. The corpus contains annotated dialogue acts, addressees, adjacency pairs and gaze direction. First, we describe the corpus design where we present the meetings collection, annotation scheme and annotation tools. Then, we present the\ud analysis of the reproducibility and stability of the annotation scheme

Computational Modeling of Face-to-Face Social Interaction Using Nonverbal Behavioral Cues

The computational modeling of face-to-face interactions using nonverbal behavioral cues is an emerging and relevant problem in social computing. Studying face-to-face interactions in small groups helps in understanding the basic processes of individual and group behavior; and improving team productivity and satisfaction in the modern workplace. Apart from the verbal channel, nonverbal behavioral cues form a rich communication channel through which people infer – often automatically and unconsciously – emotions, relationships, and traits of fellowmembers. There exists a solid body of knowledge about small groups and the multimodal nature of the nonverbal phenomenon in social psychology and nonverbal communication. However, the problem has only recently begun to be studied in the multimodal processing community. A recent trend is to analyze these interactions in the context of face-to-face group conversations, using multiple sensors and make inferences automatically without the need of a human expert. These problems can be formulated in a machine learning framework involving the extraction of relevant audio, video features and the design of supervised or unsupervised learning models. While attempting to bridge social psychology, perception, and machine learning, certain factors have to be considered. Firstly, various group conversation patterns emerge at different time-scales. For example, turn-taking patterns evolve over shorter time scales, whereas dominance or group-interest trends get established over larger time scales. Secondly, a set of audio and visual cues that are not only relevant but also robustly computable need to be chosen. Thirdly, unlike typical machine learning problems where ground truth is well defined, interaction modeling involves data annotation that needs to factor in inter-annotator variability. Finally, principled ways of integrating the multimodal cues have to be investigated. In the thesis, we have investigated individual social constructs in small groups like dominance and status (two facets of the so-called vertical dimension of social relations). In the first part of this work, we have investigated how dominance perceived by external observers can be estimated by different nonverbal audio and video cues, and affected by annotator variability, the estimationmethod, and the exact task involved. In the second part, we jointly study perceived dominance and role-based status to understand whether dominant people are the ones with high status and whether dominance and status in small-group conversations be automatically explained by the same nonverbal cues. We employ speaking activity, visual activity, and visual attention cues for both the works. In the second part of the thesis, we have investigated group social constructs using both supervised and unsupervised approaches. We first propose a novel framework to characterize groups. The two-layer framework consists of a individual layer and the group layer. At the individual layer, the floor-occupation patterns of the individuals are captured. At the group layer, the identity information of the individuals is not used. We define group cues by aggregating individual cues over time and person, and use them to classify group conversational contexts – cooperative vs competitive and brainstorming vs decision-making. We then propose a framework to discover group interaction patterns using probabilistic topicmodels. An objective evaluation of ourmethodology involving human judgment and multiple annotators, showed that the learned topics indeed are meaningful, and also that the discovered patterns resemble prototypical leadership styles – autocratic, participative, and free-rein – proposed in social psychology

Towards a Technology of Nonverbal Communication

Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communication”. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future

Towards a Technology of Nonverbal Communication: Vocal Behavior in Social and Affective Phenomena

Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communicationâ€. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future