  This paper describes the NPU system submitted to Spoofing Aware Speaker
Verification Challenge 2022. We particularly focus on the \textit{backend
ensemble} for speaker verification and spoofing countermeasure from three
aspects. Firstly, besides simple concatenation, we propose circulant matrix
transformation and stacking for speaker embeddings and countermeasure
embeddings. With the stacking operation of newly-defined circulant embeddings,
we almost explore all the possible interactions between speaker embeddings and
countermeasure embeddings. Secondly, we attempt different convolution neural
networks to selectively fuse the embeddings' salient regions into channels with
convolution kernels. Finally, we design parallel attention in 1D convolution
neural networks to learn the global correlation in channel dimensions as well
as to learn the important parts in feature dimensions. Meanwhile, we embed
squeeze-and-excitation attention in 2D convolutional neural networks to learn
the global dependence among speaker embeddings and countermeasure embeddings.
Experimental results demonstrate that all the above methods are effective.
After fusion of four well-trained models enhanced by the mentioned methods, the
best SASV-EER, SPF-EER and SV-EER we achieve are 0.559\%, 0.354\% and 0.857\%
on the evaluation set respectively. Together with the above contributions, our
submission system achieves the fifth place in this challenge.
