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