1. INTRODUCTION
With the recent surge of Artificial Intelligence Generated Content (AIGC), spoofing algorithms have also gained momentum. The convenience and the quality of generating spoof speech have improved significantly. Consequently, there is an increased risk of malicious use of spoof speech. Spoof speech is now used not only to attack automatic speaker verification (ASV) systems but also for telecommunications fraud and cognitive warfare. Much work has been proposed to prevent the dangers of spoof speech, facilitated in particular by the flagship ASVspoof Challenge series [1], [2], [3], [4]. The latest ASVspoof 2021 Challenge considered the impact of cross-channel and compression codecs on spoof speech detection systems. The spoof speech countermeasure (CM) based on pre-trained wav2vec 2.0 [5] and an integrated spectro-temporal graph attention network (AASIST) [6] achieves good results on multiple datasets [7].