  The objective of this work is speaker diarisation of speech recordings 'in
the wild'. The ability to determine speech segments is a crucial part of
diarisation systems, accounting for a large proportion of errors. In this
paper, we present a simple but effective solution for speech activity detection
based on the speaker embeddings. In particular, we discover that the norm of
the speaker embedding is an extremely effective indicator of speech activity.
The method does not require an independent model for speech activity detection,
therefore allows speaker diarisation to be performed using a unified
representation for both speaker modelling and speech activity detection. We
perform a number of experiments on in-house and public datasets, in which our
method outperforms popular baselines.
