  Large AI models trained on audio data may have the potential to rapidly
classify patients, enhancing medical decision-making and potentially improving
outcomes through early detection. Existing technologies depend on limited
datasets using expensive recording equipment in high-income, English-speaking
countries. This challenges deployment in resource-constrained, high-volume
settings where audio data may have a profound impact. 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. This application
ultimately results in an audio electronic health record (voice EHR) which may
contain complex biomarkers of health from conventional voice/respiratory
features, speech patterns, and language with semantic meaning - compensating
for the typical limitations of unimodal clinical datasets. This report
introduces a consortium of partners for global work, presents the application
used for data collection, and showcases the potential of informative voice EHR
to advance the scalability and diversity of audio AI.
