  We investigate the effectiveness of generative adversarial networks (GANs)
for speech enhancement, in the context of improving noise robustness of
automatic speech recognition (ASR) systems. Prior work demonstrates that GANs
can effectively suppress additive noise in raw waveform speech signals,
improving perceptual quality metrics; however this technique was not justified
in the context of ASR. In this work, we conduct a detailed study to measure the
effectiveness of GANs in enhancing speech contaminated by both additive and
reverberant noise. Motivated by recent advances in image processing, we propose
operating GANs on log-Mel filterbank spectra instead of waveforms, which
requires less computation and is more robust to reverberant noise. While GAN
enhancement improves the performance of a clean-trained ASR system on noisy
speech, it falls short of the performance achieved by conventional multi-style
training (MTR). By appending the GAN-enhanced features to the noisy inputs and
retraining, we achieve a 7% WER improvement relative to the MTR system.
