  To address the performance gap of English ASR models on L2 English speakers,
we evaluate fine-tuning of pretrained wav2vec 2.0 models (Baevski et al., 2020;
Xu et al., 2021) on L2-ARCTIC, a non-native English speech corpus (Zhao et al.,
2018) under different training settings. We compare \textbf{(a)} models trained
with a combination of diverse accents to ones trained with only specific
accents and \textbf{(b)} results from different single-accent models. Our
experiments demonstrate the promise of developing ASR models for non-native
English speakers, even with small amounts of L2 training data and even without
a language model. Our models also excel in the zero-shot setting where we train
on multiple L2 datasets and test on a blind L2 test set.
