  Despite continuing medical advances, the rate of newborn morbidity and
mortality globally remains high, with over 6 million casualties every year. The
prediction of pathologies affecting newborns based on their cry is thus of
significant clinical interest, as it would facilitate the development of
accessible, low-cost diagnostic tools\cut{ based on wearables and smartphones}.
However, the inadequacy of clinically annotated datasets of infant cries limits
progress on this task. This study explores a neural transfer learning approach
to developing accurate and robust models for identifying infants that have
suffered from perinatal asphyxia. In particular, we explore the hypothesis that
representations learned from adult speech could inform and improve performance
of models developed on infant speech. Our experiments show that models based on
such representation transfer are resilient to different types and degrees of
noise, as well as to signal loss in time and frequency domains.
