2024/04/02 by James Anibal, Hannah Huth, Anibal, James +53
Medicine · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #Voice and Speech Disorders #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.01620
openalex publication_date 2024/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI.