  In speaker diarisation, speaker embedding extraction models often suffer from
the mismatch between their training loss functions and the speaker clustering
method. In this paper, we propose the method of spectral clustering-aware
learning of embeddings (SCALE) to address the mismatch. Specifically, besides
an angular prototype cal (AP) loss, SCALE uses a novel affinity matrix loss
which directly minimises the error between the affinity matrix estimated from
speaker embeddings and the reference. SCALE also includes p-percentile
thresholding and Gaussian blur as two important hyper-parameters for spectral
clustering in training. Experiments on the AMI dataset showed that speaker
embeddings obtained with SCALE achieved over 50% relative speaker error rate
reductions using oracle segmentation, and over 30% relative diarisation error
rate reductions using automatic segmentation when compared to a strong baseline
with the AP-loss-based speaker embeddings.
