  Local dialects influence people to pronounce words of the same language
differently from each other. The great variability and complex characteristics
of accents creates a major challenge for training a robust and accent-agnostic
automatic speech recognition (ASR) system. In this paper, we introduce a
cross-accented English speech recognition task as a benchmark for measuring the
ability of the model to adapt to unseen accents using the existing CommonVoice
corpus. We also propose an accent-agnostic approach that extends the
model-agnostic meta-learning (MAML) algorithm for fast adaptation to unseen
accents. Our approach significantly outperforms joint training in both
zero-shot, few-shot, and all-shot in the mixed-region and cross-region settings
in terms of word error rate.
