  The wav2vec 2.0 and integrated spectro-temporal graph attention network
(AASIST) based countermeasure achieves great performance in speech
anti-spoofing. However, current spoof speech detection systems have fixed
training and evaluation durations, while the performance degrades significantly
during short utterance evaluation. To solve this problem, AASIST can be
improved to AASIST2 by modifying the residual blocks to Res2Net blocks. The
modified Res2Net blocks can extract multi-scale features and improve the
detection performance for speech of different durations, thus improving the
short utterance evaluation performance. On the other hand, adaptive large
margin fine-tuning (ALMFT) has achieved performance improvement in short
utterance speaker verification. Therefore, we apply Dynamic Chunk Size (DCS)
and ALMFT training strategies in speech anti-spoofing to further improve the
performance of short utterance evaluation. Experiments demonstrate that the
proposed AASIST2 improves the performance of short utterance evaluation while
maintaining the performance of regular evaluation on different datasets.
