SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
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.
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Kyudan Jung
claimed_verified
2026-03-24T09:22:00.564Z
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Kyudan Jung
Kyudan
user
Kyudan
69c1faa1c105f45cb0481e17
Jihwan Kim
claimed_verified
2026-03-24T13:21:00.155Z
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Jihwan Kim
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user
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69c1faa1c105f45cb0481e18
Minwoo Lee
69c1faa1c105f45cb0481e19
Soyoon Kim
69c1faa1c105f45cb0481e1a
Jeonghoon Kim
69c1faa1c105f45cb0481e1b
Jaegul Choo
69c1faa1c105f45cb0481e1c
Cheonbok Park
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
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.
A speaker-nulling framework called SNAP is proposed to reduce speaker entanglement in speech encoders, enabling detectors to focus on artifact-related patterns for improved deepfake detection performance.
self-supervised learning speech encoder deepfake detection speaker entanglement speaker-nulling framework speaker subspace orthogonal projection synthesis artifacts