Many deep learning synthetic speech generation tools are readily available.
The use of synthetic speech has caused financial fraud, impersonation of
people, and misinformation to spread. For this reason forensic methods that can
detect synthetic speech have been proposed. Existing methods often overfit on
one dataset and their performance reduces substantially in practical scenarios
such as detecting synthetic speech shared on social platforms. In this paper we
propose, Patched Spectrogram Synthetic Speech Detection Transformer (PS3DT), a
synthetic speech detector that converts a time domain speech signal to a
mel-spectrogram and processes it in patches using a transformer neural network.
We evaluate the detection performance of PS3DT on ASVspoof2019 dataset. Our
experiments show that PS3DT performs well on ASVspoof2019 dataset compared to
other approaches using spectrogram for synthetic speech detection. We also
investigate generalization performance of PS3DT on In-the-Wild dataset. PS3DT
generalizes well than several existing methods on detecting synthetic speech
from an out-of-distribution dataset. We also evaluate robustness of PS3DT to
detect telephone quality synthetic speech and synthetic speech shared on social
platforms (compressed speech). PS3DT is robust to compression and can detect
telephone quality synthetic speech better than several existing methods.

我们提出了一种名为 Patched Spectrogram Synthetic Speech Detection Transformer (PS3DT) 的合成语音检测器，通过将时间域语音信号转换成 mel 频谱图，并使用变压器神经网络在片段中处理该频谱图，来检测合成语音。我们评估了 PS3DT 在 ASVspoof2019 数据集上的检测性能，并研究了 PS3DT 在 In-the-Wild 数据集上的推广性能。PS3DT 对于压缩和电话质量合成语音具有较好的鲁棒性，比其他方法更好地检测电话质量合成语音。

基于修补频谱变换器的压缩鲁棒合成语音检测

Compression Robust Synthetic Speech Detection Using Patched Spectrogram  Transformer

The task of synthetic speech generation is to generate language content from
a given text, then simulating fake human voice.The key factors that determine
the effect of synthetic speech generation mainly include speed of generation,
accuracy of word segmentation, naturalness of synthesized speech, etc. This
paper builds an end-to-end multi-module synthetic speech generation model,
including speaker encoder, synthesizer based on Tacotron2, and vocoder based on
WaveRNN. In addition, we perform a lot of comparative experiments on different
datasets and various model structures. Finally, we won the first place in the
ADD 2023 challenge Track 1.1 with the weighted deception success rate (WDSR) of
44.97%.

本文建立了一个端到端的多模块合成语音生成模型，包括说话者编码器、基于 Tacotron2 的合成器和基于 WaveRNN 的声码器，并在不同数据集和模型结构上进行了大量比较实验。最终，我们在 ADD 2023 挑战赛中荣获第一名，加权欺骗成功率为 44.97％。