  The speech enhancement task usually consists of removing additive noise or
reverberation that partially mask spoken utterances, affecting their
intelligibility. However, little attention is drawn to other, perhaps more
aggressive signal distortions like clipping, chunk elimination, or
frequency-band removal. Such distortions can have a large impact not only on
intelligibility, but also on naturalness or even speaker identity, and require
of careful signal reconstruction. In this work, we give full consideration to
this generalized speech enhancement task, and show it can be tackled with a
time-domain generative adversarial network (GAN). In particular, we extend a
previous GAN-based speech enhancement system to deal with mixtures of four
types of aggressive distortions. Firstly, we propose the addition of an
adversarial acoustic regression loss that promotes a richer feature extraction
at the discriminator. Secondly, we also make use of a two-step adversarial
training schedule, acting as a warm up-and-fine-tune sequence. Both objective
and subjective evaluations show that these two additions bring improved speech
reconstructions that better match the original speaker identity and
naturalness.
