SBIR Phase II: Augmenting Virtual Healthcare with Voice Biomarkers

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to develop smart, robust healthcare infrastructure in the U.S. by leveraging machine learning and artificial intelligence to streamline clinical decision support. Voice biomarkers detect a variety of health conditions, emotions, and diseases and provide a unique, seamless feedback for real-time triage.

Transforming voice intonations into voice biomarkers would allow disease prediction and monitoring. The proposed voice biomarker technology is potentially a scalable behavioral health screener to provide equitable care in all virtual care visits, mitigating the complex and costly (2-3X) comorbidities of depression and anxiety in 80% of $3T in chronic conditions.

This Small Business Innovation Research (SBIR) Phase II project is dedicated to providing scalable mental health screening in primary care. The research objectives are to understand the underlying behavioral health triggers for chronic health conditions from global voice biomarker data combined with unique, longitudinal metadata. The major technical challenges in this proposed research include (1) collecting sufficiently diverse metadata labels on environmental and physiological variables,(2) training distinct models based on gender, age, and other features that have high variance through principal component analysis, (3) identifying and minimizing bias for sparse populations in design, validation, and deployment phases, and (4) improving the current voice biomarker diagnostic on dimensions of sensitivity, specificity, and diagnosability in various call center, telehealth platform, remote patient monitoring, and care management platform modalities.

The highly complex deployments across infrastructure in healthcare require multiple models tuned for specific health populations and a deep understanding of classical and deep learning techniques for improving both accuracy and generalizability across unseen populations. The anticipated technical results in solving this series of highly challenging machine learning tasks is profound for real-time triage and access to reliable mental healthcare at scale.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.