@inproceedings{park-2026-intent,
    title = "Intent vs. Surface: Recovering Acoustic Realization from {M}odern {ASR} for Pronunciation Training",
    author = "Park, Seongjin",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.23/",
    pages = "336--346",
    ISBN = "979-8-89176-409-5",
    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 phoneme-level 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."
}