@inproceedings{kim-etal-2026-diagnosis,
    title = "Diagnosis of Dysarthria Severity and Explanation Generation Using {XAI}-Enhanced {CLINIC}-{GENIE} on Diadochokinetic Tasks",
    author = "Kim, Jihyeon  and
      Lee, Insung  and
      Koo, Myoung-Wan",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.275/",
    pages = "5202--5222",
    ISBN = "979-8-89176-386-9",
    abstract = "Deep neural network classifiers for dysarthria impairment severity face limitations regarding interpretability and treatment guidance. To overcome these, we introduce CLINIC-GENIE, an explainable two-stage framework consisting of: (1) CLINIC, a dysarthria severity classification model combining acoustic and speech embeddings with Clinically Explainable Acoustic Features (CEAFs); and (2) GENIE, a module translating CEAFs and their Shapley values into intuitive natural language explanations via a large language model. CLINIC achieved a balanced accuracy of 0.952 (17.3{\%} improvement over using CEAFs alone), and certified speech-language pathologists rated explanations from CLINIC-GENIE with an average fidelity score of 4.94, confirming enhanced clinical utility."
}