  While improvements have been made in automatic speech recognition performance
over the last several years, machines continue to have significantly lower
performance on accented speech than humans. In addition, the most significant
improvements on accented speech primarily arise by overwhelming the problem
with hundreds or even thousands of hours of data. Humans typically require much
less data to adapt to a new accent. This paper explores methods that are
inspired by human perception to evaluate possible performance improvements for
recognition of accented speech, with a specific focus on recognizing speech
with a novel accent relative to that of the training data. Our experiments are
run on small, accessible datasets that are available to the research community.
We explore four methodologies: pre-exposure to multiple accents, grapheme and
phoneme-based pronunciations, dropout (to improve generalization to a novel
accent), and the identification of the layers in the neural network that can
specifically be associated with accent modeling. Our results indicate that
methods based on human perception are promising in reducing WER and
understanding how accented speech is modeled in neural networks for novel
accents.
