  In current two-stage neural text-to-speech (TTS) paradigm, it is ideal to
have a universal neural vocoder, once trained, which is robust to imperfect
mel-spectrogram predicted from the acoustic model. To this end, we propose
Robust MelGAN vocoder by solving the original multi-band MelGAN's metallic
sound problem and increasing its generalization ability. Specifically, we
introduce a fine-grained network dropout strategy to the generator. With a
specifically designed over-smooth handler which separates speech signal intro
periodic and aperiodic components, we only perform network dropout to the
aperodic components, which alleviates metallic sounding and maintains good
speaker similarity. To further improve generalization ability, we introduce
several data augmentation methods to augment fake data in the discriminator,
including harmonic shift, harmonic noise and phase noise. Experiments show that
Robust MelGAN can be used as a universal vocoder, significantly improving sound
quality in TTS systems built on various types of data.
