# Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2507.08626 · HTML: https://mlsanity.com/paper/2507.08626/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection](https://mlsanity.com/paper/2601.23066/) (2026)
- [CUPID: Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection](https://mlsanity.com/paper/2606.20302/) (2026)
- [Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction](https://mlsanity.com/paper/2607.12584/) (2026)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)

## Abstract (arXiv)

Recent advances in generative AI have made the creation of speech deepfakes widely accessible, posing serious challenges to digital trust. To counter this, various speech deepfake detection strategies have been proposed, including Person-of-Interest (POI) approaches, which focus on identifying impersonations of specific individuals by modeling and analyzing their unique vocal traits. Despite their excellent performance, the existing methods offer limited granularity and lack interpretability. In this work, we propose a POI-based speech deepfake detection method that operates at the phoneme level. Our approach decomposes reference audio into phonemes to construct a detailed speaker profile. In inference, phonemes from a test sample are individually compared against this profile, enabling fine-grained detection of synthetic artifacts. The proposed method achieves comparable accuracy to traditional approaches while offering superior robustness and interpretability, key aspects in multimedia forensics. By focusing on phoneme analysis, this work explores a novel direction for explainable, speaker-centric deepfake detection.