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 diagnostic accuracy and patient privacy. We propose an adversarial learning architecture that disentangles pathology-related acoustic patterns from speaker-identifiable attributes through gradient reversal-based 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, the adversarial model significantly reduces speaker separability, while improving or maintaining classification performance. Specifically, it significantly improves the AUC for exacerbation type classification (from 0.65 to 0.73), enhances imbalance-sensitive metrics for respiratory status screening (PR-AUC and macro F1), and maintains cross-speaker generalization under the subject-wise split. Concurrently, the J-ratio decreases, confirming effective suppression of speaker information. This study demonstrates that adversarial disentanglement of speaker identity not only enhances patient privacy but also maintains or improves diagnostic accuracy for respiratory disease monitoring. Our framework offers a foundation for deployable, interpretable, and privacy-aware speech-based methods in chronic respiratory disease management.

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