Linguistic Biomarkers

Linguistic biomarkers leverage patterns in language use to identify and understand various psychological states and conditions. Current research focuses on developing machine learning models, often employing personalized approaches, to analyze linguistic features like emotional expression and vocabulary choice in text and speech data to detect conditions such as anxiety and depression. This research aims to improve the accuracy and efficiency of mental health assessments, potentially leading to earlier interventions and more effective personalized treatments. The development of robust cross-lingual models is also a key area of focus, expanding the applicability of these methods across diverse populations.