  In recent years, neural vocoders have surpassed classical speech generation
approaches in naturalness and perceptual quality of the synthesized speech.
Computationally heavy models like WaveNet and WaveGlow achieve best results,
while lightweight GAN models, e.g. MelGAN and Parallel WaveGAN, remain inferior
in terms of perceptual quality. We therefore propose StyleMelGAN, a lightweight
neural vocoder allowing synthesis of high-fidelity speech with low
computational complexity. StyleMelGAN employs temporal adaptive normalization
to style a low-dimensional noise vector with the acoustic features of the
target speech. For efficient training, multiple random-window discriminators
adversarially evaluate the speech signal analyzed by a filter bank, with
regularization provided by a multi-scale spectral reconstruction loss. The
highly parallelizable speech generation is several times faster than real-time
on CPUs and GPUs. MUSHRA and P.800 listening tests show that StyleMelGAN
outperforms prior neural vocoders in copy-synthesis and Text-to-Speech
scenarios.
