  Despite rapid advances in speech recognition, current models remain brittle
to superficial perturbations to their inputs. Small amounts of noise can
destroy the performance of an otherwise state-of-the-art model. To harden
models against background noise, practitioners often perform data augmentation,
adding artificially-noised examples to the training set, carrying over the
original label. In this paper, we hypothesize that a clean example and its
superficially perturbed counterparts shouldn't merely map to the same class ---
they should map to the same representation. We propose
invariant-representation-learning (IRL): At each training iteration, for each
training example,we sample a noisy counterpart. We then apply a penalty term to
coerce matched representations at each layer (above some chosen layer). Our key
results, demonstrated on the Librispeech dataset are the following: (i) IRL
significantly reduces character error rates (CER) on both 'clean' (3.3% vs
6.5%) and 'other' (11.0% vs 18.1%) test sets; (ii) on several out-of-domain
noise settings (different from those seen during training), IRL's benefits are
even more pronounced. Careful ablations confirm that our results are not simply
due to shrinking activations at the chosen layers.
