  This paper describes the deepfake audio detection system submitted to the
Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis
of score fusion. The proposed system is a score-level fusion of several light
convolutional neural network (LCNN) based models. Various front-ends are used
as input features, including low-frequency short-time Fourier transform and
Constant Q transform. Due to the complex noise and rich synthesis algorithms,
it is difficult to obtain the desired performance using the training set
directly. Online data augmentation methods effectively improve the robustness
of fake audio detection systems. In particular, the reasons for the poor
improvement of score fusion are explored through visualization of the score
distributions and comparison with score distribution on another dataset. The
overfitting of the model to the training set leads to extreme values of the
scores and low correlation of the score distributions, which makes score fusion
difficult. Fusion with partially fake audio detection system improves system
performance further. The submission on track 3.2 obtained the weighted equal
error rate (WEER) of 11.04\%, which is one of the best performing systems in
the challenge.
