Interruption Audio & Transcript: Derived from Group Affect and Performance Dataset

Abstract

Despite the widespread development and use of chatbots, there is a lack of audio-based interruption datasets. This study provides a dataset of 200 manually annotated interruptions from a broader set of 355 data points of overlapping utterances. The dataset is derived from the Group Affect and Performance dataset managed by the University of the Fraser Valley, Canada. It includes both audio files and transcripts, allowing for multi-modal analysis. Given the extensive literature and the varied definitions of interruptions, it was necessary to establish precise definitions. The study aims to provide a comprehensive dataset for researchers to build and improve interruption prediction models. The findings demonstrate that classification models can generalize well to identify interruptions based on this dataset’s audio. This opens up research avenues with respect to interruption-related topics, ranging from multi-modal interruption classification using text and audio modalities to the analysis of group dynamics.