We propose a stacked U-NET architecture to automatically segment the tongue, velum, and airway in speech MRI based on hybrid learning. Three separate U-nets are trained to learn the mapping between the input image and their specific articulator. The two U-NETs to segment the velum, and tongue are based on transfer learning, where we leverage open-source brain MRI segmentation. The third U-NET for airway segmentation is based on classical training methods. We demonstrate the utility of our approach by comparing against manual segmentations.