  Existing approaches for anti-spoofing in automatic speaker verification (ASV)
still lack generalizability to unseen attacks. The Res2Net approach designs a
residual-like connection between feature groups within one block, which
increases the possible receptive fields and improves the system's detection
generalizability. However, such a residual-like connection is performed by a
direct addition between feature groups without channel-wise priority. We argue
that the information across channels may not contribute to spoofing cues
equally, and the less relevant channels are expected to be suppressed before
adding onto the next feature group, so that the system can generalize better to
unseen attacks. This argument motivates the current work that presents a novel,
channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a
channel-wise gating mechanism in the connection between feature groups. This
gating mechanism dynamically selects channel-wise features based on the input,
to suppress the less relevant channels and enhance the detection
generalizability. Three gating mechanisms with different structures are
proposed and integrated into Res2Net. Experimental results conducted on
ASVspoof 2019 logical access (LA) demonstrate that the proposed CG-Res2Net
significantly outperforms Res2Net on both the overall LA evaluation set and
individual difficult unseen attacks, which also outperforms other
state-of-the-art single systems, depicting the effectiveness of our method.
