Utilizing Deep Learning on Limited Mobile Speech Recordings for Detection of Obstructive Pulmonary Disease

Passive assessment of obstructive pulmonary disease has gained substantial interests over the past few years in the mobile and wearable computing communities. One of the promising approaches is speech-based pulmonary assessment where spontaneous or scripted speech is used to evaluate an individuals pulmonary conditions. Recent work in speech-based pulmonary assessment approach has shown promising results in pulmonary disease detection. However, this approach heavily relies on the accuracy of speech activity detection and a handful number of specific features. Recently, the application of deep learning has shown promising results in the domain of activity recognition involving time series data. In this paper, we
present a deep learning approach for detecting obstructive pulmonary disease.