A Computational Study on Sentence-based Next Speaker Prediction in Multiparty Conversations

Abstract

In this paper we present a computational study to quantitatively examine the task of predicting the next speaker in multi-party conversations using machine learning models. To accomplish this, we create features that accurately represent information relevant to speaker changes in such conversations. We utilize sentence-based models, rather than the widely-used InterPausal Unit (IPU)-based models, and extend the definition of verbal backchanneling to include additional reactions that signify listeners’ attention or interest. Through extensive experiments with various machine learning models and inputs, we show that our sentence-based models outperform existing IPU-based models, with the best model achieving 61.39% accuracy. Our study provides design implications and recommendations for the development of virtual agents or humanoid robots with interactive social interaction capabilities.