End-to-end speech models often have millions of parameters [7, 8,<cite> 9,</cite> 10, 11] . However, data augmentation and dropout have not been extensively studied or applied to them. We investigate the effectiveness of data augmentation and dropout for regularizing end-to-end speech models.