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Speech enhancement is an essential component in robust automatic
speech recognition (ASR) systems. Most speech enhancement
methods are nowadays based on neural networks
that use feature-mapping or mask-learning. This paper proposes
a novel speech enhancement method that integrates time-domain
feature mapping and mask learning into a unified framework
using a Generative Adversarial Network (GAN). The proposed
framework processes the received waveform and decouples
speech and noise signals, which are fed into two short-time
Fourier transform (STFT) convolution 1-D layers that map
the waveforms to spectrograms in the complex domain. These
speech and noise spectrograms are then used to compute the
speech mask loss. The proposed method is evaluated using the
TIMIT data set for seen and unseen signal-to-noise ratio conditions.
It is shown that the proposed method outperforms the
speech enhancement methods that use Deep Neural Network
(DNN) based speech enhancement or a Speech Enhancement
Generative Adversarial Network (SEGAN).
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