Detection of mild dyspnea from pairs of speech recordings

Shortness of breath, or dyspnea is a condition of the cardiopulmonary system that may be caused by, for example, a heart or lung disease, or physical load. In this paper, we explore techniques of detecting mild dyspnea directly from conversational speech, for example, in a telehealth application. We demonstrate with a collection of speech recordings before and after a light physical exercise that a siamese neural network, when presented examples of the two conditions, can detect the difference between two speech signals. This shows that this signal can be detected using data-pairs, removing the need for ratings of severity or the distinction of separate classes.