  Previous generative adversarial network (GAN)-based neural vocoders are
trained to reconstruct the exact ground truth waveform from the paired
mel-spectrogram and do not consider the one-to-many relationship of speech
synthesis. This conventional training causes overfitting for both the
discriminators and the generator, leading to the periodicity artifacts in the
generated audio signal. In this work, we present PhaseAug, the first
differentiable augmentation for speech synthesis that rotates the phase of each
frequency bin to simulate one-to-many mapping. With our proposed method, we
outperform baselines without any architecture modification. Code and audio
samples will be available at https://github.com/mindslab-ai/phaseaug.
