  Recently, many novel techniques have been introduced to deal with spoofing
attacks, and achieve promising countermeasure (CM) performances. However, these
works only take the stand-alone CM models into account. Nowadays, a spoofing
aware speaker verification (SASV) challenge which aims to facilitate the
research of integrated CM and ASV models, arguing that jointly optimizing CM
and ASV models will lead to better performance, is taking place. In this paper,
we propose a novel multi-model and multi-level fusion strategy to tackle the
SASV task. Compared with purely scoring fusion and embedding fusion methods,
this framework first utilizes embeddings from CM models, propagating CM
embeddings into a CM block to obtain a CM score. In the second-level fusion,
the CM score and ASV scores directly from ASV systems will be concatenated into
a prediction block for the final decision. As a result, the best single fusion
system has achieved the SASV-EER of 0.97% on the evaluation set. Then by
ensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.
