Twente Debate Corpus - A Multimodal Corpus for Head Movement Analysis

This paper introduces a multimodal discussion corpus for the study into head movement and turn-taking patterns in debates. Given that participants either acted alone or in a pair, cooperation and competition and their nonverbal correlates can be analyzed. In addition to the video and audio of the recordings, the corpus contains automatically estimated head movements, and manual annotations of who is speaking and who is looking where. The corpus consists of over 2 hours of debates, in 6 groups with 18 participants in total. We describe the recording setup and present initial analyses of the recorded data. We found that the person who acted as single debater speaks more and also receives more attention compared to the other debaters, also when corrected for the time speaking.We also found that a single debater was more likely to speak after a team debater. Future work will be aimed at further analysis of the relation between speaking and looking patterns, the outcome of the debate and perceived dominance of the debaters

Modeling dominance effects on nonverbal behaviors using granger causality

In this paper we modeled the effects that dominant people might induce on the nonverbal behavior (speech energy and body motion) of the other meeting participants using Granger causality technique. Our initial hypothesis that more dominant people have generalized higher influence was not validated when using the DOME-AMI corpus as data source. However, from the correlational analysis some interesting patterns emerged: contradicting our initial hypothesis dominant individuals are not accounting for the majority of the causal flow in a social interaction. Moreover, they seem to have more intense causal effects as their causal density was significantly higher. Finally dominant individuals tend to respond to the causal effects more often with complementarity than with mimicry

Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users’ experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

An Exploratory Assessment Of Small Group Performance Leveraging Motion Dynamics With Optical Flow

Understanding team behaviors and dynamics are important to better understand and foster better teamwork. The goal of this master\u27s thesis was to contribute to understanding and assessing teamwork in small group research, by analyzing motion dynamics and team performance with non-contact sensing and computational assessment. This thesis\u27s goal is to conduct an exploratory analysis of motion dynamics on teamwork data to understand current limitations in data gathering approaches and provide a methodology to automatically categorize, label, and code team metrics from multi-modal data. We created a coding schema that analyzed different teamwork datasets. We then produced a taxonomy of the metrics from the literature that classify teamwork behaviors and performance. These metrics were grouped on whether they measured communication dynamics or movement dynamics. The review showed movement dynamics in small group research is a potential area to apply more robust computational sensing and detection approaches. To enhance and demonstrate the importance of motion dynamics, we analyzed video and transcript data on a publicly available multi-modal dataset. We determined areas for future study where movement dynamics are potentially correlated to team behaviors and performance. We processed the video data into movement dynamic time series data using an optical flow approach to track and measure motion from the data. Audio data was measured by speaking turns, words used, and keywords used, which were defined as our communication dynamics. Our exploratory analysis demonstrated a correlation between the group performance score using communication dynamics metrics, along with movement dynamics metrics. This assessment provided insights for sensing data capture strategies and computational analysis for future small group research studies

Brand mimicry of luxury brands

This research extends the theory of mimicry from the discipline of biological and natural sciences to the luxury brand context. Three brand mimicry scales namely Wicklerian-Eisnerian, Vavilovian and Pouyannian mimicry were developed and validated. A conceptual model is developed to test the influences of the three types of brand mimicry across four categories of luxury products. The findings provide academics, practitioners and policy makers with valuable insights into mimicry in the luxury brand industry