  This work investigates Artificial Intelligence (AI) systems that detect
respiratory insufficiency (RI) by analyzing speech audios, thus treating speech
as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients
during the first phase of the pandemic and trained modern AI models, such as
CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the
feasibility of RI detection via AI. Here, we collect RI patient data (P2) with
several causes besides COVID-19, aiming at extending AI-based RI detection. We
also collected control data from hospital patients without RI. We show that the
considered models, when trained on P1, do not generalize to P2, indicating that
COVID-19 RI has features that may not be found in all RI types.
