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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces RRP-Voice, the first longitudinal voice corpus for Recurrent Respiratory Papillomatosis, providing a critical resource for advancing voice pathology detection. The comprehensive benchmarking and longitudinal analysis contribute significantly to the field, addressing gaps in existing research and offering a foundation for future studies in rare disease diagnostics.
Methodology
The methodology is robust, introducing a longitudinal dataset that addresses a significant gap in the study of rare laryngeal diseases. The systematic benchmarking across various representation families, including handcrafted features and modern deep learning approaches, demonstrates a comprehensive approach to evaluating voice pathology detection. The use of synchronous laryngoscopic labels adds credibility to the dataset and ensures that the results are clinically relevant.
Results & evaluation
The experimental setup is thorough, employing a well-structured cross-validation approach that preserves session integrity. The results show clear distinctions between different methods, particularly highlighting the effectiveness of self-supervised models over traditional supervised baselines. The longitudinal analysis provides valuable insights into the dynamics of voice pathology, which is often overlooked in cross-sectional studies.
Reproducibility
The paper provides sufficient details on the experimental setup, including model architectures, training procedures, and evaluation metrics, which enhances reproducibility. However, the absence of a publicly accessible dataset or code repository limits the ability for other researchers to replicate the findings directly.
Limitations
The dataset is relatively small, with only 26 patients contributing to the recordings, which may limit the generalizability of the findings. Additionally, the study focuses primarily on cross-sectional classification without delving deeply into longitudinal predictive modeling, which could be a valuable extension.
Broader impact
This work has significant implications for clinical practices in monitoring rare laryngeal diseases, potentially leading to improved patient outcomes through better diagnostic tools. The introduction of a longitudinal dataset also sets a precedent for future research in low-resource clinical settings, encouraging the exploration of other rare diseases using similar methodologies. The paper introduces RRP-Voice, the first longitudinal voice corpus for Recurrent Respiratory Papillomatosis, providing a critical resource for advancing voice pathology detection. The comprehensive benchmarking and longitudinal analysis contribute significantly to the field, addressing gaps in existing research and offering a foundation for future studies in rare disease diagnostics.
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