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Data augmentation is a ubiquitous technique used to provide robustness toautomatic speech recognition (ASR) training. However, even as so much of theASR training process has become automated and more "end-to-end", the dataaugmentation policy (what augmentation functions to use, and how to apply them)remains hand-crafted. We present Graph-Augment, a technique to define theaugmentation space as directed acyclic graphs (DAGs) and search over this spaceto optimize the augmentation policy itself. We show that given the samecomputational budget, policies produced by G-Augment are able to perform betterthan SpecAugment policies obtained by random search on fine-tuning tasks onCHiME-6 and AMI. G-Augment is also able to establish a new state-of-the-art ASRperformance on the CHiME-6 evaluation set (30.7% WER). We further demonstratethat G-Augment policies show better transfer properties across warm-start tocold-start training and model size compared to random-searched SpecAugmentpolicies.

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR