  Recent years have witnessed the extraordinary development of automatic
speaker verification (ASV). However, previous works show that state-of-the-art
ASV models are seriously vulnerable to voice spoofing attacks, and the recently
proposed high-performance spoofing countermeasure (CM) models only focus solely
on the standalone anti-spoofing tasks, and ignore the subsequent speaker
verification process. How to integrate the CM and ASV together remains an open
question. A spoofing aware speaker verification (SASV) challenge has recently
taken place with the argument that better performance can be delivered when
both CM and ASV subsystems are optimized jointly. Under the challenge's
scenario, the integrated systems proposed by the participants are required to
reject both impostor speakers and spoofing attacks from target speakers, which
intuitively and effectively matches the expectation of a reliable,
spoofing-robust ASV system. This work focuses on fusion-based SASV solutions
and proposes a multi-model fusion framework to leverage the power of multiple
state-of-the-art ASV and CM models. The proposed framework vastly improves the
SASV-EER from 8.75% to 1.17\%, which is 86% relative improvement compared to
the best baseline system in the SASV challenge.
