  Classical parametric speech coding techniques provide a compact
representation for speech signals. This affords a very low transmission rate
but with a reduced perceptual quality of the reconstructed signals. Recently,
autoregressive deep generative models such as WaveNet and SampleRNN have been
used as speech vocoders to scale up the perceptual quality of the reconstructed
signals without increasing the coding rate. However, such models suffer from a
very slow signal generation mechanism due to their sample-by-sample modelling
approach. In this work, we introduce a new methodology for neural speech
vocoding based on generative adversarial networks (GANs). A fake speech signal
is generated from a very compressed representation of the glottal excitation
using conditional GANs as a deep generative model. This fake speech is then
refined using the LPC parameters of the original speech signal to obtain a
natural reconstruction. The reconstructed speech waveforms based on this
approach show a higher perceptual quality than the classical vocoder
counterparts according to subjective and objective evaluation scores for a
dataset of 30 male and female speakers. Moreover, the usage of GANs enables to
generate signals in one-shot compared to autoregressive generative models. This
makes GANs promising for exploration to implement high-quality neural vocoders.
