COPD and asthma cause significant morbidity and mortality across the globe. The development of non-invasive telemonitoring solutions offers the potential to improve patient care.

To examine whether breathing and speech patterns extracted from audio recordings enhance model generalizability of distinguishing stable from exacerbation periods in COPD and asthma.

Patterns related to breathing and speech were extracted from a dataset that combined daily audio speech recordings with a patient-reported outcome measure (EXACT). Medical experts categorized patient health status as stable or exacerbation. A tree-based model was investigated for exacerbation prediction. The model was trained on signal-related features, breathing and speech patterns, and combination of both. Results were examined for variability among all people and feature importance.

The study included 21 people (mean age 62.7 years; 66.7% female), 61.9% diagnosed with COPD and 38.1% with asthma. Minimum breath duration and speech duration differed between stable and exacerbation states (p < 0.05). The model, trained on combined features sets achieved Sensitivity of 0.78, 0.10 SD exceeding models trained on acoustic-only (0.70, 0.17 SD) and patterns-only sets (0.68, 0.13 SD). Inconsistencies were observed in patient-level results across exacerbation severity. Speech Duration, Spectral Contrast, Harmonic to Noise Ratio, Breath Group Duration and 26 Mel-Frequency Cepstral Coefficient were found to have the highest importance for the best performing model.

Combining signal derived features with breathing and speech patterns helps models to achieve higher results. Detected clusters within the study group indicate patient-level variability. Presented results position breathing and speech patterns as valuable tool for remote patient screening.