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In recent years, neural vocoders have surpassed classical speech generationapproaches 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 inferiorin terms of perceptual quality. We therefore propose StyleMelGAN, a lightweightneural vocoder allowing synthesis of high-fidelity speech with lowcomputational complexity. StyleMelGAN employs temporal adaptive normalizationto style a low-dimensional noise vector with the acoustic features of thetarget speech. For efficient training, multiple random-window discriminatorsadversarially evaluate the speech signal analyzed by a filter bank, withregularization provided by a multi-scale spectral reconstruction loss. Thehighly parallelizable speech generation is several times faster than real-timeon CPUs and GPUs. MUSHRA and P.800 listening tests show that StyleMelGANoutperforms prior neural vocoders in copy-synthesis and Text-to-Speechscenarios.

StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization