@inproceedings{park-etal-2025-compositional,
    title = "Compositional Phoneme Approximation for {L}1-Grounded {L}2 Pronunciation Training",
    author = "Park, Jisang  and
      Kim, Minu  and
      Hong, DaYoung  and
      Lee, Jongha",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-short.35/",
    pages = "434--443",
    ISBN = "979-8-89176-299-2",
    abstract = "Learners of a second language (L2) often map non-native phonemes to similar native-language (L1) phonemes, making conventional L2-focused training slow and effortful. To address this, we propose an L1-grounded pronunciation training method based on compositional phoneme approximation (CPA), a feature-based representation technique that approximates L2 sounds with sequences of L1 phonemes.Evaluations with 20 Korean non-native English speakers show that CPA-based training achieves a 76{\%} in-box formant rate in acoustic analysis, 17.6{\%} relative improvement in phoneme recognition accuracy, and over 80{\%} of speech being rated as more native-like, with minimal training. Project page: \url{https://gsanpark.github.io/CPA-Pronunciation}."
}