DeepFake Audio, unlike DeepFake images and videos, has been relatively less
explored from detection perspective, and the solutions which exist for the
synthetic speech classification either use complex networks or dont generalize
to different varieties of synthetic speech obtained using different generative
and optimization-based methods. Through this work, we propose a channel-wise
recalibration of features using attention feature fusion for synthetic speech
detection and compare its performance against different detection methods
including End2End models and Resnet-based models on synthetic speech generated
using Text to Speech and Vocoder systems like WaveNet, WaveRNN, Tactotron, and
WaveGlow. We also experiment with Squeeze Excitation (SE) blocks in our Resnet
models and found that the combination was able to get better performance. In
addition to the analysis, we also demonstrate that the combination of Linear
frequency cepstral coefficients (LFCC) and Mel Frequency cepstral coefficients
(MFCC) using the attentional feature fusion technique creates better input
features representations which can help even simpler models generalize well on
synthetic speech classification tasks. Our models (Resnet based using feature
fusion) trained on Fake or Real (FoR) dataset and were able to achieve 95% test
accuracy with the FoR data, and an average of 90% accuracy with samples we
generated using different generative models after adapting this framework.

本文研究了 DeepFake Audio 从检测角度进行了研究，并提出了使用注意力特征融合的通道重新校准方法进行合成语音检测。同时，我们也通过使用 SE 块和 LFCC/MFCC 的结合，改进了 Resnet 模型，并提出了更好的输入特征嵌入方式，使得即使较为简单的模型在合成语音分类任务上也能够很好地推广。经过针对 Fake or Real 数据集的训练，我们的模型在 FoR 数据上能够获得 95％的测试准确度，并在使用不同的生成模型生成样本后适应该框架后，达到了 90％的平均准确度。