arXiv Computation and Language By Xi Xuan, Wenxin Zhang, Zhiyu Li, Jennifer Williams, Ville Hautam\"aki, Tomi H. Kinnunen

Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning

Read the original on arXiv Computation and Language →

arXiv:2603. 21875v2 Announce Type: replace-cross Abstract: Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting source embeddings are independent of speaker traits.

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arXiv AI
Sep 7

SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

The paper introduces SNAP, a speaker‑nulling framework designed to improve deepfake speech detection. By estimating a speaker subspace and orthogonally projecting out speaker‑dependent components, SNAP isolates synthesis artifacts in the residual features. This reduction of speaker entanglement enables detectors to focus on artifact‑related cues, achieving state‑of‑the‑art performance.

By Kyudan Jung, Jihwan Kim, Minwoo Lee, Soyoon Kim, Jeonghoon Kim, Jaegul Choo, Cheonbok Park