Speech Biomarkers

Speech biomarkers utilize acoustic features from speech recordings to detect and monitor various health conditions, aiming to provide objective, remote, and cost-effective diagnostic tools. Current research heavily employs deep learning architectures, such as convolutional neural networks and transformers, often coupled with machine learning algorithms like logistic regression and gradient boosting, to analyze these biomarkers for conditions ranging from Parkinson's disease and Alzheimer's to respiratory insufficiency and heart failure. While promising results have been achieved in classification tasks, challenges remain in accurately estimating continuous physiological parameters from speech, highlighting the need for refined feature extraction and model development. The ultimate goal is to integrate speech-based diagnostics into clinical practice, improving early detection, personalized treatment, and patient monitoring.