Chronic obstructive pulmonary disease (COPD) and asthma are the most common respiratory diseases that impact millions of people worldwide annually. With advances in mobile computing and machine learning techniques, there has been increased interest in using mobile devices to monitor pulmonary diseases. Nevertheless, the current state-of-the-art technology requires active involvement and high-effort input from the users, impeding continuous monitoring of pulmonary conditions. In this work, two algorithms are proposed for passive assessment of pulmonary condition: one for detection of obstructive pulmonary disease and the other for estimation of the pulmonary function in terms of FEV_1/FVC ratio, which is an established clinical metric. The algorithms were developed and validated using the data sets from two studies: research study (healthy=40, pathological=91) and in-clinic study (healthy=10, pathological=60). From the cross-study validation where a classifier was trained on the research data set and tested on the in-clinic data set, the detection accuracy of the pathological class was obtained as 73.7 and the F1 score was 84.5 (87.2 precision and 82.0 recall). In our regression analysis, the FEV_1/FVC ratio was predicted with a mean absolute error of 8.6. Our analysis shows promising results and this work presents a meaningful milestone towards the passive assessment of pulmonary functions from spontaneous speech collected from a mobile phone.