  End-to-end models for robust automatic speech recognition (ASR) have not been
sufficiently well-explored in prior work. With end-to-end models, one could
choose to preprocess the input speech using speech enhancement techniques and
train the model using enhanced speech. Another alternative is to pass the noisy
speech as input and modify the model architecture to adapt to noisy speech. A
systematic comparison of these two approaches for end-to-end robust ASR has not
been attempted before. We address this gap and present a detailed comparison of
speech enhancement-based techniques and three different model-based adaptation
techniques covering data augmentation, multi-task learning, and adversarial
learning for robust ASR. While adversarial learning is the best-performing
technique on certain noise types, it comes at the cost of degrading clean
speech WER. On other relatively stationary noise types, a new speech
enhancement technique outperformed all the model-based adaptation techniques.
This suggests that knowledge of the underlying noise type can meaningfully
inform the choice of adaptation technique.

Audio and Speech Processing;Machine Learning;Sound