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

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.

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

arXiv:2610.09486 [eess.AS]

(or arXiv:2610.09486v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2610.09486 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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