  Transition Relevance Places are defined as the end of an utterance where the
interlocutor may take the floor without interrupting the current speaker
--i.e., a place where the turn is terminal. Analyzing turn terminality is
useful to study the dynamic of turn-taking in spontaneous conversations. This
paper presents an automatic classification of spoken utterances as Terminal or
Non-Terminal in multi-speaker settings. We compared audio, text, and fusions of
both approaches on a French corpus of TV and Radio extracts annotated with
turn-terminality information at each speaker change. Our models are based on
pre-trained self-supervised representations. We report results for different
fusion strategies and varying context sizes. This study also questions the
problem of performance variability by analyzing the differences in results for
multiple training runs with random initialization. The measured accuracy would
allow the use of these models for large-scale analysis of turn-taking.
