  The objective of this work is to train noise-robust speaker embeddings
adapted for speaker diarisation. Speaker embeddings play a crucial role in the
performance of diarisation systems, but they often capture spurious information
such as noise, adversely affecting performance. Our previous work has proposed
an auto-encoder-based dimensionality reduction module to help remove the
redundant information. However, they do not explicitly separate such
information and have also been found to be sensitive to hyper-parameter values.
To this end, we propose two contributions to overcome these issues: (i) a novel
dimensionality reduction framework that can disentangle spurious information
from the speaker embeddings; (ii) the use of speech activity vector to prevent
the speaker code from representing the background noise. Through a range of
experiments conducted on four datasets, our approach consistently demonstrates
the state-of-the-art performance among models without system fusion.
