LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech

• URL: http://arxiv.org/abs/2506.00628v2
• Date: Wed, 11 Jun 2025 03:08:00 GMT
• Title: LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech
• Authors: Niyati Bafna, Matthew Wiesner,
• Abstract summary: Prior research indicates that LID model performance significantly declines on accented speech.<n>We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker's native language or a related language.<n>We present an approach that integrates sequence-level information into our model without relying on monolingual ASR systems.
• Score: 4.654709537754806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior research indicates that LID model performance significantly declines on accented speech; however, the specific causes, extent, and characterization of these errors remain under-explored. (i) We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker's native language or a related language. (ii) We present evidence suggesting that state-of-the-art models are invariant to permutations of short spans of speech, implying they classify on the basis of short phonotactic features indicative of accent rather than language. Our analysis reveals a simple method to enhance model robustness to accents through input chunking. (iii) We present an approach that integrates sequence-level information into our model without relying on monolingual ASR systems; this reduces accent-language confusion and significantly enhances performance on accented speech while maintaining comparable results on standard LID.

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