Teamwork Quality Prediction Using Speech-Based Features

This paper describes a novel protocol for annotating teamwork quality and related variables, based only on the speech signal. Our protocol was designed to annotate a Spanish version of the Objects Games corpus, a publicly available corpus that contains dialogues of people playing a collaborative computer game. The corpus was annotated by 4 raters, who achieved an Intra class Correlation Coefficient of 0.64 for the main teamwork quality metric. Using the resulting annotations, we developed a system for automatic prediction of the average teamwork quality across raters using features extracted from the conversations, reaching a coefficient of determination, R2 of 0.56. This result suggests that automatic prediction of teamwork quality from the speech signal of the teammates is a feasible task

Group Interaction Frontiers in Technology

Over the last decade, the study of group behavior for multimodal interaction technologies has increased. However, we believe that despite its potential benefits on society, there could be more activity in this area. The aim of this workshop is create a forum for more interdisciplinary dialogue on this topic to enable the acceleration of growth. The workshop has been very successful in attracting submissions addressing important facets in the context of technologies for analyzing and aiding groups. This paper provides a summary of the activities of the workshop and the accepted papers

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