  Turn-taking is a fundamental aspect of human communication and can be
described as the ability to take turns, project upcoming turn shifts, and
supply backchannels at appropriate locations throughout a conversation. In this
work, we investigate the role of prosody in turn-taking using the recently
proposed Voice Activity Projection model, which incrementally models the
upcoming speech activity of the interlocutors in a self-supervised manner,
without relying on explicit annotation of turn-taking events, or the explicit
modeling of prosodic features. Through manipulation of the speech signal, we
investigate how these models implicitly utilize prosodic information. We show
that these systems learn to utilize various prosodic aspects of speech both on
aggregate quantitative metrics of long-form conversations and on single
utterances specifically designed to depend on prosody.
