Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach
This work addresses COPD detection for patients using a non-invasive speech-based approach, but it is incremental as it applies existing methods to a new dataset.
The study tackled early detection of Chronic Obstructive Pulmonary Disease (COPD) by analyzing speech from Danish participants, achieving a best accuracy of 67% using openSMILE features and logistic regression.
Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world. Its early detection using non-invasive means could enable preventive interventions that improve quality of life and patient outcomes, with speech recently shown to be a valuable biomarker. Yet, its validity across different linguistic groups remains to be seen. To that end, audio data were collected from 96 Danish participants conducting three speech tasks (reading, coughing, sustained vowels). Half of the participants were diagnosed with different levels of COPD and the other half formed a healthy control group. Subsequently, we investigated different baseline models using openSMILE features and learnt x-vector embeddings. We obtained a best accuracy of 67% using openSMILE features and logistic regression. Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions.