  The goal of this paper is to adapt speaker embeddings for solving the problem
of speaker diarisation. The quality of speaker embeddings is paramount to the
performance of speaker diarisation systems. Despite this, prior works in the
field have directly used embeddings designed only to be effective on the
speaker verification task. In this paper, we propose three techniques that can
be used to better adapt the speaker embeddings for diarisation: dimensionality
reduction, attention-based embedding aggregation, and non-speech clustering. A
wide range of experiments is performed on various challenging datasets. The
results demonstrate that all three techniques contribute positively to the
performance of the diarisation system achieving an average relative improvement
of 25.07% in terms of diarisation error rate over the baseline.
