  We present a neural vocoder designed with low-powered Alternative and
Augmentative Communication devices in mind. By combining elements of successful
modern vocoders with established ideas from an older generation of technology,
our system is able to produce high quality synthetic speech at 48kHz on devices
where neural vocoders are otherwise prohibitively complex. The system is
trained adversarially using differentiable pitch synchronous overlap add, and
reduces complexity by relying on pitch synchronous Inverse Short-Time Fourier
Transform (ISTFT) to generate speech samples. Our system achieves comparable
quality with a strong (HiFi-GAN) baseline while using only a fraction of the
compute. We present results of a perceptual evaluation as well as an analysis
of system complexity.
