# Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice

arXiv: https://arxiv.org/abs/2406.10422 · HTML: https://mlsanity.com/paper/2406.10422/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech](https://mlsanity.com/paper/2411.08013/) (2024)
- [On the reliability of feature attribution methods for speech classification](https://mlsanity.com/paper/2505.16406/) (2025)
- [XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifacts](https://mlsanity.com/paper/2609.24770/) (2026)

## Abstract (arXiv)

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.