  Modern automatic speech recognition (ASR) systems need to be robust under
acoustic variability arising from environmental, speaker, channel, and
recording conditions. Ensuring such robustness to variability is a challenge in
modern day neural network-based ASR systems, especially when all types of
variability are not seen during training. We attempt to address this problem by
encouraging the neural network acoustic model to learn invariant feature
representations. We use ideas from recent research on image generation using
Generative Adversarial Networks and domain adaptation ideas extending
adversarial gradient-based training. A recent work from Ganin et al. proposes
to use adversarial training for image domain adaptation by using an
intermediate representation from the main target classification network to
deteriorate the domain classifier performance through a separate neural
network. Our work focuses on investigating neural architectures which produce
representations invariant to noise conditions for ASR. We evaluate the proposed
architecture on the Aurora-4 task, a popular benchmark for noise robust ASR. We
show that our method generalizes better than the standard multi-condition
training especially when only a few noise categories are seen during training.
