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离散化的显著性图谱方法，该算法利用音素边界对显著性图谱进行离散化处理，以实现对AI生成语音的可解释检测。通过Tacotron2和Fastspeech2两种不同文本转语音系统的实验验证，本算法生成的显著性图谱相较于标准的后置解释方法能提供更可靠的解释。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。