  End-to-end models have achieved significant improvement on automatic speech
recognition. One common method to improve performance of these models is
expanding the data-space through data augmentation. Meanwhile, human auditory
inspired front-ends have also demonstrated improvement for automatic speech
recognisers. In this work, a well-verified auditory-based model, which can
simulate various hearing abilities, is investigated for the purpose of data
augmentation for end-to-end speech recognition. By introducing the auditory
model into the data augmentation process, end-to-end systems are encouraged to
ignore variation from the signal that cannot be heard and thereby focus on
robust features for speech recognition. Two mechanisms in the auditory model,
spectral smearing and loudness recruitment, are studied on the LibriSpeech
dataset with a transformer-based end-to-end model. The results show that the
proposed augmentation methods can bring statistically significant improvement
on the performance of the state-of-the-art SpecAugment.
