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This article addresses a critical challenge in AI safety and deepfake detection by proposing a method to decouple speaker identity from synthesis artifacts, thereby improving model generalization across unseen voices. However, the authors have voluntarily withdrawn the manuscript after identifying that their claims overstated the experimental evidence and that certain results were overinterpreted. Consequently, while the technical approach of "speaker nulling" is relevant to the field, the specific conclusions presented in this version are no longer considered reliable.
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
arXiv:2603.20686v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Upon further review, the authors identified concerns that some of the claims may overstate what is supported by the experimental evidence, and that aspects of the experimental results may have been overinterpreted. These issues affect the reliability of the paper's main conclusions. The authors therefore wish to withdraw the manuscript
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2603.20686 [cs.SD]
(or arXiv:2603.20686v2 [cs.SD] for this version)
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