# Mitigating Accent-Language Confusion in Self-Supervised Speech Representations for Language Identification

arXiv: https://arxiv.org/abs/2610.09486 · HTML: https://mlsanity.com/paper/2610.09486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 3 of 3)

- [Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces](https://mlsanity.com/paper/2603.12642/) (2026)
- [Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection](https://mlsanity.com/paper/2609.09499/) (2026)
- [Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection](https://mlsanity.com/paper/2609.16458/) (2026)

## Abstract (arXiv)

Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from self-supervised speech representations frequently confuse accents with languages, misclassifying non-native (L2) speech as the speaker's first language (L1). We show that non-native speech representations lie between native target-language and native L1 poles, causing systematic misclassification. To address this, we introduce a geometric projection that estimates an L1-bias direction solely from native speech and removes it before the frozen LID head. Across five MMS-LID models and non-native corpora, this projection substantially improves target language identification for L2-accented speech while preserving predictions for native speech. These results show that accent-induced L1 bias can be corrected directly within the representation space without L2 training data or model adaptation.