  We propose AudioStyleGAN (ASGAN), a new generative adversarial network (GAN)
for unconditional speech synthesis. As in the StyleGAN family of image
synthesis models, ASGAN maps sampled noise to a disentangled latent vector
which is then mapped to a sequence of audio features so that signal aliasing is
suppressed at every layer. To successfully train ASGAN, we introduce a number
of new techniques, including a modification to adaptive discriminator
augmentation to probabilistically skip discriminator updates. ASGAN achieves
state-of-the-art results in unconditional speech synthesis on the Google Speech
Commands dataset. It is also substantially faster than the top-performing
diffusion models. Through a design that encourages disentanglement, ASGAN is
able to perform voice conversion and speech editing without being explicitly
trained to do so. ASGAN demonstrates that GANs are still highly competitive
with diffusion models. Code, models, samples:
https://github.com/RF5/simple-asgan/.
