  We introduce and analyze a novel approach to the problem of speaker
identification in multi-party recorded meetings. Given a speech segment and a
set of available candidate profiles, we propose a novel data-driven way to
model the distance relations between them, aiming at identifying the speaker
label corresponding to that segment. To achieve this we employ a recurrent,
memory-based architecture, since this class of neural networks has been shown
to yield advanced performance in problems requiring relational reasoning. The
proposed encoding of distance relations is shown to outperform traditional
distance metrics, such as the cosine distance. Additional improvements are
reported when the temporal continuity of the audio signals and the speaker
changes is modeled in. In this paper, we have evaluated our method in two
different tasks, i.e. scripted and real-world business meeting scenarios, where
we report a relative reduction in speaker error rate of 39.28% and 51.84%,
respectively, compared to the baseline.
