  Spectral mapping uses a deep neural network (DNN) to map directly from noisy
speech to clean speech. Our previous study found that the performance of
spectral mapping improves greatly when using helpful cues from an acoustic
model trained on clean speech. The mapper network learns to mimic the input
favored by the spectral classifier and cleans the features accordingly. In this
study, we explore two new innovations: we replace a DNN-based spectral mapper
with a residual network that is more attuned to the goal of predicting clean
speech. We also examine how integrating long term context in the mimic
criterion (via wide-residual biLSTM networks) affects the performance of
spectral mapping compared to DNNs. Our goal is to derive a model that can be
used as a preprocessor for any recognition system; the features derived from
our model are passed through the standard Kaldi ASR pipeline and achieve a WER
of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using
only feature adaptation.
