Tools to generate high quality synthetic speech signal that is perceptually
indistinguishable from speech recorded from human speakers are easily
available. Several approaches have been proposed for detecting synthetic
speech. Many of these approaches use deep learning methods as a black box
without providing reasoning for the decisions they make. This limits the
interpretability of these approaches. In this paper, we propose Disentangled
Spectrogram Variational Auto Encoder (DSVAE) which is a two staged trained
variational autoencoder that processes spectrograms of speech using
disentangled representation learning to generate interpretable representations
of a speech signal for detecting synthetic speech. DSVAE also creates an
activation map to highlight the spectrogram regions that discriminate synthetic
and bona fide human speech signals. We evaluated the representations obtained
from DSVAE using the ASVspoof2019 dataset. Our experimental results show high
accuracy (>98%) on detecting synthetic speech from 6 known and 10 out of 11
unknown speech synthesizers. We also visualize the representation obtained from
DSVAE for 17 different speech synthesizers and verify that they are indeed
interpretable and discriminate bona fide and synthetic speech from each of the
synthesizers.

本文提出使用 Disentangled Spectrogram Variational Auto Encoder (DSVAE) 处理语音谱图以生成可解释的表示，并创建激活映射来区分合成和真实人类讲话信号，通过 ASVspoof2019 数据集获得高精度（>98%）检测多种语音合成器合成的语音。