{"ID":2921213,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-04T00:54:56.190393508Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01639","arxiv_id":"2606.01639","title":"RRP-Voice: A Longitudinal Dataset and Benchmark for Recurrent Respiratory Papillomatosis Detection","abstract":"Deep learning has advanced pathological voice detection rapidly, yet rare laryngeal diseases remain underexplored due to data scarcity. Recurrent Respiratory Papillomatosis (RRP) exemplifies this gap: an HPV-induced disease of the larynx in which patients oscillate between recurrence and post-surgical remission over the years. RRP demands continuous voice monitoring that existing cross-sectional corpora cannot support. We introduce the first longitudinal voice dataset for RRP, comprising recordings from 26 patients with up to ten years of follow-up. Each session pairs sustained vowels with sentence-level utterances, which are annotated by otolaryngologists and confirmed synchronously with laryngoscopy. Building on this resource, we establish a systematic benchmark spanning handcrafted features, end-to-end deep networks, self-supervised pretrained models, and recent audio large language models, all evaluated under session-level cross-validation with patient-level audit. Per-subject longitudinal analyses further confirm that the cross-sectional discriminative signal reflects laryngoscopic disease state rather than stable speaker attributes. This work lays a foundation for rare longitudinal pathological voice tasks in low-resource clinical settings.","short_abstract":"Deep learning has advanced pathological voice detection rapidly, yet rare laryngeal diseases remain underexplored due to data scarcity. Recurrent Respiratory Papillomatosis (RRP) exemplifies this gap: an HPV-induced disease of the larynx in which patients oscillate between recurrence and post-surgical remission over th...","url_abs":"https://arxiv.org/abs/2606.01639","url_pdf":"https://arxiv.org/pdf/2606.01639v1","authors":"[\"Wenze Ren\",\"Ke-Han Lu\",\"Kai-Wei Chang\",\"Tiantian Feng\",\"Ching Fang\",\"Zhi-Chi Liao\",\"Dao Thi Hai Yen\",\"Syu-Siang Wang\",\"Yu Tsao\",\"Chi-Te Wang\",\"Shih-Hau Fang\"]","published":"2026-06-01T03:45:22Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[\"Language Model\"]","has_code":false}