  The rhythm of bonafide speech is often difficult to replicate, which causes
that the fundamental frequency (F0) of synthetic speech is significantly
different from that of real speech. It is expected that the F0 feature contains
the discriminative information for the fake speech detection (FSD) task. In
this paper, we propose a novel F0 subband for FSD. In addition, to effectively
model the F0 subband so as to improve the performance of FSD, the spatial
reconstructed local attention Res2Net (SR-LA Res2Net) is proposed.
Specifically, Res2Net is used as a backbone network to obtain multiscale
information, and enhanced with a spatial reconstruction mechanism to avoid
losing important information when the channel group is constantly superimposed.
In addition, local attention is designed to make the model focus on the local
information of the F0 subband. Experimental results on the ASVspoof 2019 LA
dataset show that our proposed method obtains an equal error rate (EER) of
0.47% and a minimum tandem detection cost function (min t-DCF) of 0.0159,
achieving the state-of-the-art performance among all of the single systems.
