  The performance of speech processing models trained on clean speech drops
significantly in noisy conditions. Training with noisy datasets alleviates the
problem, but procuring such datasets is not always feasible. Noisy speech
simulation models that generate noisy speech from clean speech help remedy this
issue. In our work, we study the ability of Generative Adversarial Networks
(GANs) to simulate a variety of noises. Noise from the
Ultra-High-Frequency/Very-High-Frequency (UHF/VHF), additive stationary and
non-stationary, and codec distortion categories are studied. We propose four
GANs, including the non-parallel translators, SpeechAttentionGAN, SimuGAN, and
MaskCycleGAN-Augment, and the parallel translator, Speech2Speech-Augment. We
achieved improvements of 55.8%, 28.9%, and 22.8% in terms of Multi-Scale
Spectral Loss (MSSL) as compared to the baseline for the RATS, TIMIT-Cabin, and
TIMIT-Helicopter datasets, respectively, after training on small datasets of
about 3 minutes.
