  We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint
waveform generation method using a generative adversarial network. In the
proposed method, a non-autoregressive WaveNet is trained by jointly optimizing
multi-resolution spectrogram and adversarial loss functions, which can
effectively capture the time-frequency distribution of the realistic speech
waveform. As our method does not require density distillation used in the
conventional teacher-student framework, the entire model can be easily trained.
Furthermore, our model is able to generate high-fidelity speech even with its
compact architecture. In particular, the proposed Parallel WaveGAN has only
1.44 M parameters and can generate 24 kHz speech waveform 28.68 times faster
than real-time on a single GPU environment. Perceptual listening test results
verify that our proposed method achieves 4.16 mean opinion score within a
Transformer-based text-to-speech framework, which is comparative to the best
distillation-based Parallel WaveNet system.
