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

arXiv: https://arxiv.org/abs/2609.16458 · HTML: https://mlsanity.com/paper/2609.16458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 25 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Comparative layer-wise analysis of self-supervised speech models](https://mlsanity.com/paper/2211.03929/) (2022)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [MLAAD: The Multi-Language Audio Anti-Spoofing Dataset](https://mlsanity.com/paper/2401.09512/) (2024)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces](https://mlsanity.com/paper/2603.12642/) (2026)
- [Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis](https://mlsanity.com/paper/2501.06810/) (2025)
- [Are audio DeepFake detection models polyglots?](https://mlsanity.com/paper/2412.17924/) (2024)
- [SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian](https://mlsanity.com/paper/2509.19865/) (2025)
- [Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection](https://mlsanity.com/paper/2609.09499/) (2026)
- [How Far Do SSL Speech Models Listen for Tone? Temporal Focus of Tone Representation under Low-resource Transfer](https://mlsanity.com/paper/2511.12285/) (2025)
- [When Spoof Detectors Travel: Evaluation Across 66 Languages in the Low-Resource Language Spoofing Corpus](https://mlsanity.com/paper/2603.02364/) (2026)
- [Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster](https://mlsanity.com/paper/2603.07238/) (2026)

## Built on by (most cited first, 1 of 1)

- [Mitigating Accent-Language Confusion in Self-Supervised Speech Representations for Language Identification](https://mlsanity.com/paper/2610.09486/) (2026)

## Abstract (arXiv)

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.