A. Agrawala

CL-DAF is presented, a Cross-Lingual Disease-Alignment Framework that identifies acoustic dimensions whose disease effects remain consistent across languages and provides a foundation for multilingual clinical speech models emphasizing pathology over language-dependent variation.

SpiroPhonia, a machine learning framework that leverages spontaneous speech for respiratory health assessment, is introduced and it is demonstrated that everyday speech encodes robust respiratory biomarkers, paving the way for continuous health monitoring via voice-enabled technologies.