@inproceedings{yan-etal-2025-hippo,
    title = "{H}i{PPO}: Exploring A Novel Hierarchical Pronunciation Assessment Approach for Spoken Languages",
    author = "Yan, Bi-Cheng  and
      Wang, Hsin Wei  and
      Chao, Fu-An  and
      Lo, Tien-Hong  and
      Hsu, Yung-Chang  and
      Chen, Berlin",
    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-long.45/",
    pages = "810--823",
    ISBN = "979-8-89176-298-5",
    abstract = "Automatic pronunciation assessment (APA) seeks to quantify a second language (L2) learner{'}s pronunciation proficiency in a target language by offering timely and fine-grained diagnostic feedback. Most existing efforts on APA have predominantly concentrated on highly constrained reading-aloud tasks (where learners are prompted to read a reference text aloud); however, assessing pronunciation quality in unscripted speech (or free-speaking scenarios) remains relatively underexplored. In light of this, we first propose HiPPO, a hierarchical pronunciation assessment model tailored for spoken languages, which evaluates an L2 learner{'}s oral proficiency at multiple linguistic levels based solely on the speech uttered by the learner. To improve the overall accuracy of assessment, a contrastive ordinal regularizer and a curriculum learning strategy are introduced for model training. The former aims to generate score-discriminative features by exploiting the ordinal nature of regression targets, while the latter gradually ramps up the training complexity to facilitate the assessment task that takes unscripted speech as input. Experiments conducted on the Speechocean762 benchmark dataset validates the feasibility and superiority of our method in relation to several cutting-edge baselines."
}