  Speaker embedding extractors (EEs), which map input audio to a speaker
discriminant latent space, are of paramount importance in speaker diarisation.
However, there are several challenges when adopting EEs for diarisation, from
which we tackle two key problems. First, the evaluation is not straightforward
because the features required for better performance differ between speaker
verification and diarisation. We show that better performance on widely adopted
speaker verification evaluation protocols does not lead to better diarisation
performance. Second, embedding extractors have not seen utterances in which
multiple speakers exist. These inputs are inevitably present in speaker
diarisation because of overlapped speech and speaker changes; they degrade the
performance. To mitigate the first problem, we generate speaker verification
evaluation protocols that mimic the diarisation scenario better. We propose two
data augmentation techniques to alleviate the second problem, making embedding
extractors aware of overlapped speech or speaker change input. One technique
generates overlapped speech segments, and the other generates segments where
two speakers utter sequentially. Extensive experimental results using three
state-of-the-art speaker embedding extractors demonstrate that both proposed
approaches are effective.
