  Spoofing countermeasure (CM) and automatic speaker verification (ASV)
sub-systems can be used in tandem with a backend classifier as a solution to
the spoofing aware speaker verification (SASV) task. The two sub-systems are
typically trained independently to solve different tasks. While our previous
work demonstrated the potential of joint optimisation, it also showed a
tendency to over-fit to speakers and a lack of sub-system complementarity.
Using only a modest quantity of auxiliary data collected from new speakers, we
show that joint optimisation degrades the performance of separate CM and ASV
sub-systems, but that it nonetheless improves complementarity, thereby
delivering superior SASV performance. Using standard SASV evaluation data and
protocols, joint optimisation reduces the equal error rate by 27\% relative to
performance obtained using fixed, independently-optimised sub-systems under
like-for-like training conditions.
