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Recently, many novel techniques have been introduced to deal with spoofingattacks, and achieve promising countermeasure (CM) performances. However, theseworks only take the stand-alone CM models into account. Nowadays, a spoofingaware speaker verification (SASV) challenge which aims to facilitate theresearch of integrated CM and ASV models, arguing that jointly optimizing CMand 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 theSASV task. Compared with purely scoring fusion and embedding fusion methods,this framework first utilizes embeddings from CM models, propagating CMembeddings 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 intoa prediction block for the final decision. As a result, the best single fusionsystem has achieved the SASV-EER of 0.97% on the evaluation set. Then byensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.

Spoofing-Aware Speaker Verification by Multi-Level Fusion