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 phonotraumatic (PVH) and non-phonotraumatic (NPVH) from healthy controls. We propose a hierarchical feature engineering framework comprising: (i) static, (ii) dynamic, (iii) ratio-based, (iv) coupling features capturing source filter interactions. While univariate statistical analysis shows strong separability for PVH but limited significance for NPVH, our machine learning pipeline, tailored for high-dimensional feature integration, identifies that coupling features are crucial for both tasks. We achieve an AUC of 0.891 for PVH and 0.728 for NPVH, suggesting that while PVH is near-linearly separable, NPVH discrimination benefits from modeling non-linear feature interactions.

翻译：可穿戴颈部加速度信号可无创监测嗓音亢进，然而其亚型的稳健生物标志物仍较为有限。本研究基于NeckVibe挑战数据集，探索区分音创伤性嗓音亢进（PVH）、非音创伤性嗓音亢进（NPVH）与健康对照组的特征模式。我们提出包含以下四类特征的层级化特征工程框架：（i）静态特征、（ii）动态特征、（iii）比率特征、（iv）表征声源-滤波器交互的耦合特征。单变量统计分析显示，PVH组呈现强可分性，而NPVH组统计显著性有限；但面向高维特征集成优化的机器学习流程表明，耦合特征对两种分类任务均至关重要。最终模型在PVH分类中达到0.891的AUC值，在NPVH分类中达到0.728的AUC值，这表明PVH具有近似线性可分性，而NPVH的判别则得益于对非线性特征交互的建模。