  The objective of this work is effective speaker diarisation using multi-scale
speaker embeddings. Typically, there is a trade-off between the ability to
recognise short speaker segments and the discriminative power of the embedding,
according to the segment length used for embedding extraction. To this end,
recent works have proposed the use of multi-scale embeddings where segments
with varying lengths are used. However, the scores are combined using a
weighted summation scheme where the weights are fixed after the training phase,
whereas the importance of segment lengths can differ with in a single session.
To address this issue, we present three key contributions in this paper: (1) we
propose graph attention networks for multi-scale speaker diarisation; (2) we
design scale indicators to utilise scale information of each embedding; (3) we
adapt the attention-based aggregation to utilise a pre-computed affinity matrix
from multi-scale embeddings. We demonstrate the effectiveness of our method in
various datasets where the speaker confusion which constitutes the primary
metric drops over 10% in average relative compared to the baseline.
