  Predicting turn-taking in multiparty conversations has many practical
applications in human-computer/robot interaction. However, the complexity of
human communication makes it a challenging task. Recent advances have shown
that synchronous multi-perspective egocentric data can significantly improve
turn-taking prediction compared to asynchronous, single-perspective
transcriptions. Building on this research, we propose a new multimodal
transformer-based architecture for predicting turn-taking in embodied,
synchronized multi-perspective data. Our experimental results on the recently
introduced EgoCom dataset show a substantial performance improvement of up to
14.01% on average compared to existing baselines and alternative
transformer-based approaches. The source code, and the pre-trained models of
our 3M-Transformer will be available upon acceptance.
