Detection of COPD Exacerbation from Speech: Comparison of Acoustic Features and Deep Learning Based Speech Breathing Models

Abstract

Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients’ breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.