Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection

Abstract:Audio deepfake detectors need to transfer to languages absent from training, as multilingual speech synthesis outpaces labeled anti-spoofing resources. While detectors increasingly rely on self-supervised speech models (S3Ms), these backbones encode language-dependent structure that confounds spoof cues. We address this confound through language orthogonalization, a target-free ridge map that removes S3M variation projected onto continuous language-identification (LID) embeddings. Across six languages, six S3M backbones, and all Leave-N-Out settings, it consistently reduces EER across unseen languages. Cross-lingual EER correlates with LID-space distance, where orthogonalization yields larger gains for more distant transfers.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)

arXiv:2609.16458 [eess.AS]

(or arXiv:2609.16458v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.16458 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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