Compression Robust Synthetic Speech Detection Using Patched Spectrogram
  Transformer
Amit Kumar Singh Yadav Ziyue Xiang Kratika Bhagtani Paolo Bestagini Stefano Tubaro Edward J. Delp
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.