  Domain mismatch between training and testing can lead to significant
degradation in performance in many machine learning scenarios. Unfortunately,
this is not a rare situation for automatic speech recognition deployments in
real-world applications. Research on robust speech recognition can be regarded
as trying to overcome this domain mismatch issue. In this paper, we address the
unsupervised domain adaptation problem for robust speech recognition, where
both source and target domain speech are presented, but word transcripts are
only available for the source domain speech. We present novel
augmentation-based methods that transform speech in a way that does not change
the transcripts. Specifically, we first train a variational autoencoder on both
source and target domain data (without supervision) to learn a latent
representation of speech. We then transform nuisance attributes of speech that
are irrelevant to recognition by modifying the latent representations, in order
to augment labeled training data with additional data whose distribution is
more similar to the target domain. The proposed method is evaluated on the
CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as
35% compared to the non-adapted baseline.
