# SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2603.20686 · HTML: https://mlsanity.com/paper/2603.20686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens](https://mlsanity.com/paper/2407.05407/) (2024)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training](https://mlsanity.com/paper/2505.17589/) (2025)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2404.02781/) (2024)
- [DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors](https://mlsanity.com/paper/2406.11427/) (2024)
- [Source Tracing of Audio Deepfake Systems](https://mlsanity.com/paper/2407.08016/) (2024)
- [An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio](https://mlsanity.com/paper/2208.09646/) (2022)
- [WavLM model ensemble for audio deepfake detection](https://mlsanity.com/paper/2408.07414/) (2024)
- [How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?](https://mlsanity.com/paper/2406.02483/) (2024)
- [STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution](https://mlsanity.com/paper/2505.19644/) (2025)
- [Source Verification for Speech Deepfakes](https://mlsanity.com/paper/2505.14188/) (2025)
- [TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes](https://mlsanity.com/paper/2506.05802/) (2025)
- [Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals](https://mlsanity.com/paper/2306.01861/) (2023)
- [Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis](https://mlsanity.com/paper/2406.10836/) (2024)
- [SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures](https://mlsanity.com/paper/2407.00913/) (2024)
- [SA-WavLM: Speaker-Aware Self-Supervised Pre-training for Mixture Speech](https://mlsanity.com/paper/2407.02826/) (2024)
- [Synthetic Speech Source Tracing using Metric Learning](https://mlsanity.com/paper/2506.02590/) (2025)
- [Open-Set Source Tracing of Audio Deepfake Systems](https://mlsanity.com/paper/2507.06470/) (2025)
- [Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation](https://mlsanity.com/paper/2505.19273/) (2025)
- [Detecting Synthetic Speech Manipulation in Real Audio Recordings](https://mlsanity.com/paper/2209.07498/) (2022)

## Abstract (arXiv)

Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the efficacy of self-supervised learning-based speech encoders for deepfake detection, these models struggle to generalize across unseen speakers. Our quantitative analysis suggests these encoder representations are substantially influenced by speaker information, causing detectors to exploit speaker-specific correlations rather than artifact-related cues. We call this phenomenon speaker entanglement. To mitigate this reliance, we introduce SNAP, a speaker-nulling framework. We estimate a speaker subspace and apply orthogonal projection to suppress speaker-dependent components, isolating synthesis artifacts within the residual features. By reducing speaker entanglement, SNAP encourages detectors to focus on artifact-related patterns, leading to state-of-the-art performance.