  Recently, GAN vocoders have seen rapid progress in speech synthesis, starting
to outperform autoregressive models in perceptual quality with much higher
generation speed. However, autoregressive vocoders are still the common choice
for neural generation of speech signals coded at very low bit rates. In this
paper, we present a GAN vocoder which is able to generate wideband speech
waveforms from parameters coded at 1.6 kbit/s. The proposed model is a modified
version of the StyleMelGAN vocoder that can run in frame-by-frame manner,
making it suitable for streaming applications. The experimental results show
that the proposed model significantly outperforms prior autoregressive vocoders
like LPCNet for very low bit rate speech coding, with computational complexity
of about 5 GMACs, providing a new state of the art in this domain. Moreover,
this streamwise adversarial vocoder delivers quality competitive to advanced
speech codecs such as EVS at 5.9 kbit/s on clean speech, which motivates
further usage of feed-forward fully-convolutional models for low bit rate
speech coding.
