BackgroundVocal biomarker–based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. ObjectiveThis study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). MethodsA logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%;