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Speech enhancement techniques that use a generative adversarial
network (GAN) can effectively suppress noise while allowing
models to be trained end-to-end. However, such techniques
directly operate on time-domain waveforms, which are
often highly-dimensional and require extensive computation.
This paper proposes a novel GAN-based speech enhancement
method, referred to as S-ForkGAN, that operates on log-power
spectra rather than on time-domain speech waveforms, and uses
a forked GAN structure to extract both speech and noise information.
By operating on log-power spectra, one can seamlessly
include conventional spectral subtraction techniques, and the
parameter space typically has a lower dimension. The performance
of S-ForkGAN is assessed for automatic speech recognition
(ASR) using the TIMIT data set and a wide range of noise
conditions. It is shown that S-ForkGAN outperforms existing
GAN-based techniques and that it has a lower complexity.
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