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arXiv · 2510.10738

Proficiency-Aware Adaptation and Data Augmentation for Robust L2 ASR

Ling Sun·Charlotte Zhu·Shuju Shi
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Abstract

General-purpose ASR underperforms for atypical speakers, such as L2 learners, reinforcing bias and limiting use in education and accessibility. Using the CEFR-graded Speak and Improve corpus, we show that naive fine-tuning of Whisper reduces average WER but simultaneously widens disparities and disproportionately harms lower-level learners. To address this, we propose two strategies: (i) proficiency-aware multitask learning, jointly optimizing ASR with proficiency classification, and (ii) targeted augmentation, applying spectrogram masking to low-proficiency speech to counter imbalance. These approaches reduce WER by up to 29.4 percent (relative) and insertion/deletion errors by as much as 58.6 percent (relative). Crucially, despite the severe imbalance of the dataset reflecting real-world distributions, both strategies consistently narrow proficiency gaps, advancing equitable ASR for L2 learners.

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Ling Sun, Charlotte Zhu, Shuju Shi. 2025-10-12. Proficiency-Aware Adaptation and Data Augmentation for Robust L2 ASR. https://arxiv.org/abs/2510.10738

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