Speech analysis can effectively classify respiratory disorders using advanced machine learning techniques, improving diagnostic accuracy.
The review identifies key biomarker features related to respiratory health that enhance classification algorithms in this context.
A comprehensive examination of existing literature reveals significant advances in deep learning applications for speech analysis in respiratory conditions.
These findings suggest a transformative potential for non-invasive diagnostic methods using speech patterns, although further validation is necessary.