In this paper, we propose Phoneme Discretized Saliency Maps (PDSM), a discretization algorithm for saliency maps that takes advantage of phoneme boundaries for explainable detection of AI-generated voice. We experimentally show with two different Text-to-Speech systems (i.e., Tacotron2 and Fastspeech2) that the proposed algorithm produces saliency maps that result in more faithful explanations compared to standard posthoc explanation methods. Moreover, by associating the saliency maps to the phoneme representations, this methodology generates explanations that tend to be more understandable than standard saliency maps on magnitude spectrograms.

翻译：本文提出了一种基于音素边界离散化的显著性图谱算法（PDSM），用于实现AI生成语音的可解释检测。通过Tacotron2和Fastspeech2两种文本转语音系统的实验验证，相较于标准的后验解释方法，该算法生成的显著性图谱能够提供更可靠的解释。此外，通过将显著性图谱与音素表征相关联，该方法生成的解释比基于幅度谱图的标准显著性图谱更具可理解性。