  The existing fake audio detection systems often rely on expert experience to
design the acoustic features or manually design the hyperparameters of the
network structure. However, artificial adjustment of the parameters can have a
relatively obvious influence on the results. It is almost impossible to
manually set the best set of parameters. Therefore this paper proposes a fully
automated end-toend fake audio detection method. We first use wav2vec
pre-trained model to obtain a high-level representation of the speech.
Furthermore, for the network structure, we use a modified version of the
differentiable architecture search (DARTS) named light-DARTS. It learns deep
speech representations while automatically learning and optimizing complex
neural structures consisting of convolutional operations and residual blocks.
The experimental results on the ASVspoof 2019 LA dataset show that our proposed
system achieves an equal error rate (EER) of 1.08%, which outperforms the
state-of-the-art single system.
