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

2026-10-07 05:42:40
Minu Kim, Jihwan Lee, David R. Mortensen, Shrikanth Narayanan
arXiv_CL
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2610.09486

PDF

https://arxiv.org/pdf/2610.09486.pdf

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