Early detection of exacerbations in asthma and chronic obstructive pulmonary disease (COPD) is important for timely intervention. Speech has emerged as a promising tool for continuous, non-invasive respiratory disease monitoring. However, speech signals inherently carry speaker-identifiable attributes that may dominate model predictions, which may compromise both diagnosis performance and patient privacy. Furthermore, the acoustic features associated with respiratory disease and speaker identity remain unclear in respiratory disease monitoring. We propose an adversarial learning architecture that disentangles pathology-related acoustic patterns from speaker-identifiable attributes. The framework optimizes two clinically hierarchical tasks: (i) respiratory status classification (stable vs. exacerbated) and (ii) exacerbation type classification (asthma exacerbation vs. COPD exacerbation). Speaker identity is suppressed through gradient reversal-based adversarial training. To enhance clinical interpretability, we employ SHapley Additive exPlanations (SHAP) to quantify the contributions of acoustic features to pathology-related predictions versus speaker identity. On the TACTICAS dataset, our method outperforms the single-task baseline across both tasks. For the respiratory status task (stable vs. exacerbated), the AUC improves from 0.897 to 0.910. For the exacerbation type task (asthma exacerbation vs. COPD exacerbation), the AUC increases from 0.674 to 0.793. Concurrently, the J-ratio decreases, confirming effective suppression of speaker information. SHAP analysis reveals the contributions of the acoustic features to both tasks. External validation on the Bridge2AI-Voice dataset further demonstrates consistent performance improvement and reduced speaker dependency, confirming cross-dataset generalizability.

翻译：哮喘和慢性阻塞性肺疾病（COPD）急性加重的早期检测对于及时干预至关重要。语音已成为一种有前景的持续、无创呼吸疾病监测工具。然而，语音信号本质上携带说话人可识别属性，这些属性可能主导模型预测，从而损害诊断性能和患者隐私。此外，在呼吸疾病监测中，与呼吸疾病和说话人身份相关的声学特征尚不明确。我们提出了一种对抗学习架构，用于将病理相关声学模式与说话人可识别属性解耦。该框架优化了两个临床分层任务：（i）呼吸状态分类（稳定期 vs. 急性加重期）和（ii）急性加重类型分类（哮喘急性加重 vs. 慢阻肺急性加重）。通过基于梯度反转的对抗训练抑制说话人身份。为增强临床可解释性，我们采用SHapley加法解释（SHAP）来量化声学特征对病理相关预测与说话人身份预测的贡献。在TACTICAS数据集上，我们的方法在两个任务上均优于单任务基线。对于呼吸状态任务（稳定期 vs. 急性加重期），AUC从0.897提升至0.910。对于急性加重类型任务（哮喘急性加重 vs. 慢阻肺急性加重），AUC从0.674升至0.793。同时，J-比率下降，证实了说话人信息的有效抑制。SHAP分析揭示了声学特征对两个任务的贡献。在Bridge2AI-Voice数据集上的外部验证进一步展示了性能的持续提升和说话人依赖性的降低，证实了跨数据集的泛化能力。