SpeechSpiro: Lung Function Assessment from Speech Pattern as an Alternative to Spirometry for Mobile Health Tracking

Respiratory illnesses are common in the United States and globally; people deal with these illnesses in various forms, such as asthma, chronic obstructive pulmonary diseases, or infectious respiratory diseases (e.g., coronavirus). The lung function of subjects affected by these illnesses degrades due to infection or inflammation in their respiratory airways. Typically, lung function is assessed using in-clinic medical equipment, and quite recently, via portable spirometry devices. Research has shown that the obstruction and restriction in the respiratory airways affect individuals' voice characteristics. Hence, audio features could play a role in predicting the lung function and severity of the obstruction. In this paper, we go beyond well-known voice audio features and create a hybrid deep learning model using CNN-LSTM to discover spatiotemporal patterns in speech and predict the lung function parameters with accuracy comparable to conventional devices. We validate the performance and generalizability of our method using the data collected from 201 subjects enrolled in two studies internally and in collaboration with a pulmonary hospital. SpeechSpiro measures lung function parameters (e.g., forced vital capacity) with a mean normalized RMSE of 12% and R² score of up to 76% using 60-second phone audio recordings of individuals reading a passage.Clinical relevance - Speech-based spirometry has the potential to eliminate the need for an additional device to carry out the lung function assessment outside clinical settings; hence, it can enable continuous and mobile track of the individual's condition, healthy or with a respiratory illness, using a smartphone.