DSVAE：用于合成语音检测的可解释解耦表示
DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection
AI总结：
本文提出两阶段训练的解耦频谱图变分自编码器（DSVAE），通过解耦表示学习生成可解释的语音表示并创建激活图，在ASVspoof2019数据集上对已知和未知合成语音检测准确率超98%，解决了黑盒模型缺乏可解释性的问题。
摘要：
生成与人类说话者录音在感知上无法区分的高质量合成语音信号的工具很容易获取。目前已提出多种检测合成语音的方法。其中许多方法将深度学习方法作为黑盒使用，未提供其决策的推理过程。这限制了这些方法的可解释性。在本文中，我们提出了解耦频谱图变分自编码器（DSVAE），这是一种两阶段训练的变分自编码器，它使用解耦表示学习处理语音频谱图，以生成用于检测合成语音的语音信号可解释表示。DSVAE还创建一个激活图，以突出区分合成语音和真实人类语音信号的频谱图区域。我们使用ASVspoof2019数据集评估了从DSVAE获得的表示。我们的实验结果显示，在检测来自6个已知和11个未知语音合成器中10个的合成语音方面具有高准确率（>98%）。我们还可视化了从DSVAE获得的17种不同语音合成器的表示，并验证它们确实具有可解释性，并且能够区分真实语音和来自各个合成器的合成语音。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
