Post-stroke dysphagia is common and associated with significant morbidity and mortality. Existing tools to assess for dysphagia include the gold standard (Barium swallow test) or screening tools administered by trained professionals. Each approach presents draw-backs including costs, human-resource requirements and subjectivity. Patients often must wait for swallowing assessments and are prohibited from intaking food orally, negatively impacting their quality of life and outcomes. In this study, we examine the application of convolutional neural networks (CNNs) to rapidly classify patient swallowing status using voice samples alone.