  In recent years, synthetic speech generated by advanced text-to-speech (TTS)
and voice conversion (VC) systems has caused great harms to automatic speaker
verification (ASV) systems, urging us to design a synthetic speech detection
system to protect ASV systems. In this paper, we propose a new speech
anti-spoofing model named ResWavegram-Resnet (RW-Resnet). The model contains
two parts, Conv1D Resblocks and backbone Resnet34. The Conv1D Resblock is based
on the Conv1D block with a residual connection. For the first part, we use the
raw waveform as input and feed it to the stacked Conv1D Resblocks to get the
ResWavegram. Compared with traditional methods, ResWavegram keeps all the
information from the audio signal and has a stronger ability in extracting
features. For the second part, the extracted features are fed to the backbone
Resnet34 for the spoofed or bonafide decision. The ASVspoof2019 logical access
(LA) corpus is used to evaluate our proposed RW-Resnet. Experimental results
show that the RW-Resnet achieves better performance than other state-of-the-art
anti-spoofing models, which illustrates its effectiveness in detecting
synthetic speech attacks.
