Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

When the words are not everything: the use of laughter, fillers, back-channel, silence, and overlapping speech in phone calls

This article presents an observational study on how some common conversational cues – laughter, fillers, back-channel, silence, and overlapping speech – are used during mobile phone conversations. The observations are performed over the SSPNet Mobile Corpus, a collection of 60 calls between pairs of unacquainted individuals (120 subjects for roughly 12 h of material in total). The results show that the temporal distribution of the social signals above is not uniform, but it rather reflects the social meaning they carry and convey. In particular, the results show significant use differences depending on factors such as gender, role (caller or receiver), topic, mode of interaction (agreement or disagreement), personality traits, and conflict handling style

Modelling Participant Affect in Meetings with Turn-Taking Features

This paper explores the relationship between turn-taking and meeting affect. To investigate this, we model post-meeting ratings of satisfaction, cohesion and leadership from participants of AMI corpus meetings using group and individual turn-taking features. The results indicate that participants gave higher satisfaction and cohesiveness ratings to meetings with greater group turn-taking freedom and individual very short utterance rates, while lower ratings were associated with more silence and speaker overlap. Besides broad applicability to satisfaction ratings, turn-taking freedom was found to be a better predictor than equality of speaking time when considering whether participants felt that everyone they had a chance to contribute. If we include dialogue act information, we see that substantive feedback type turns like assessments are more predictive of meeting affect than information giving acts or backchannels. This work highlights the importance of feedback turns and modelling group level activity in multiparty dialogue for understanding the social aspects of speech

Detección automática de la dominancia en conversaciones diádicas

Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers’ opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.La dominancia está relacionada con el nivel de influencia que una persona tiene en una conversación. El estudio de la dominancia es de especial interés en la psicología social, pero el problema de su estimación automática es un tema muy reciente en los contextos de computación social e inalámbrica. En este trabajo nos centramos en la detección de dominancia a partir del análisis automático de características visuales. Hacemos una estimación de la correlación entre los observadores al categorizar las personas dominantes en un conjunto de conversaciones cara a cara. Definimos diferentes indicadores de dominancia a partir de información gestual, los cuales también son anotados manualmente y comparados con la opinión de los observadores. Además, los indicadores considerados son extraídos de forma automática de las secuencias de vídeo y aprendidos mediante clasificadores binarios. Los resultados de los tres análisis muestran un alto grado de correlación y permiten categorizar de forma automática las personas dominantes en vídeos públicos de debates

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure