Intent vs. Surface: Recovering Acoustic Realization from Modern ASR for Pronunciation Training
Year: 2026
Type: conference-paper
Abstract: Pronunciation feedback in language learning depends on accurate detection of learner errors, but it is unclear whether modern ASR systems are suitable for this purpose.Their language models recover intended words rather than what was actually pronounced, systematically masking mispronunciations.This is a tendency we call intent bias.By evaluating eight ASR systems spanning three architectures on two L2 English corpora, we find that overcorrection rate correlates inversely with word error rate.In other words, ASR systems with lower WER tend to mask more pronunciation errors.We propose surface-faithful reranking, an inference-time method that uses phonemelevel acoustic similarity to select N-best hypotheses closer to what the learner actually said.Without retraining or access to model internals, the method reduces the false acceptance rate of mispronunciations by 6.0 percentage points on L2-ARCTIC and 5.6 on speechocean762.The improvement is consistent across age groups and first-language backgrounds, though substantial overcorrection remains, pointing to the need for pronunciation-aware ASR objectives.
Author: Seongjin Park
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166871946