  This work analyzes how attention-based Bidirectional Long Short-Term Memory
(BLSTM) models adapt to noise-augmented speech. We identify crucial components
for noise adaptation in BLSTM models by freezing model components during
fine-tuning. We first freeze larger model subnetworks and then pursue a
fine-grained freezing approach in the encoder after identifying its importance
for noise adaptation. The first encoder layer is shown to be crucial for noise
adaptation, and the weights are shown to be more important than the other
layers. Appreciable accuracy benefits are identified when fine-tuning on a
target noisy environment from a model pretrained with noisy speech relative to
fine-tuning from a model pretrained with only clean speech when tested on the
target noisy environment. For this analysis, we produce our own dataset
augmentation tool and it is open-sourced to encourage future efforts in
exploring noise adaptation in ASR.
