FairTalk: Facilitating Balanced Participation in Video Conferencing by Implicit Visualization of Predicted Turn-Grabbing Intention

TL;DR

FairTalk is a system designed to promote fair participation in video conferences by implicitly visualizing participants' turn-taking intentions using machine learning, aiming to subtly balance speaking opportunities.

Contribution

It introduces a novel machine learning approach to predict turn intentions and visualizes these predictions to facilitate balanced participation without explicit prompts.

Findings

Potential improvement in speaking balance during video calls

Participants' subjective feedback shows no significant perceived impact

Design implications discussed based on user interviews

Abstract

Creating fair opportunities for all participants to contribute is a notable challenge in video conferencing. This paper introduces FairTalk, a system that facilitates the subconscious redistribution of speaking opportunities. FairTalk predicts participants' turn-grabbing intentions using a machine learning model trained on web-collected videoconference data with positive-unlabeled learning, where turn-taking detection provides automatic positive labels. To subtly balance speaking turns, the system visualizes predicted intentions by mimicking natural human behaviors associated with the desire to speak. A user study suggests that FairTalk may help improve speaking balance, though subjective feedback indicates no significant perceived impact. We also discuss design implications derived from participant interviews.