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AI framework classifies vocal hyperfunction subtypes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a hierarchical feature engineering framework to automatically classify vocal hyperfunction subtypes using neck-surface acceleration data. This method integrates static, dynamic, ratio-based, and coupling features to distinguish between phonotraumatic (PVH), non-phonotraumatic (NPVH), and healthy vocal patterns. The framework achieved an AUC of 0.891 for PVH and 0.728 for NPVH, highlighting the importance of coupling features for accurate classification. AI

IMPACT Introduces a novel AI-driven approach for medical diagnosis in speech pathology.

RANK_REASON This is a research paper detailing a new framework and its performance on a specific classification task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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AI framework classifies vocal hyperfunction subtypes

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This is a research paper detailing a new framework and its performance on a specific classification task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · June-Woo Kim, Kangwook Jang, Minu Kim, Hyunju Lee ·

    A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction

    arXiv:2606.07673v1 Announce Type: cross Abstract: Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the NeckVibe Challenge dataset to distinguish phonotraumat…

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