  Nowadays vast amounts of speech data are recorded from low-quality recorder
devices such as smartphones, tablets, laptops, and medium-quality microphones.
The objective of this research was to study the automatic generation of
high-quality speech from such low-quality device-recorded speech, which could
then be applied to many speech-generation tasks. In this paper, we first
introduce our new device-recorded speech dataset then propose an improved
end-to-end method for automatically transforming the low-quality
device-recorded speech into professional high-quality speech. Our method is an
extension of a generative adversarial network (GAN)-based speech enhancement
model called speech enhancement GAN (SEGAN), and we present two modifications
to make model training more robust and stable. Finally, from a large-scale
listening test, we show that our method can significantly enhance the quality
of device-recorded speech signals.
