  We propose Universal MelGAN, a vocoder that synthesizes high-fidelity speech
in multiple domains. To preserve sound quality when the MelGAN-based structure
is trained with a dataset of hundreds of speakers, we added multi-resolution
spectrogram discriminators to sharpen the spectral resolution of the generated
waveforms. This enables the model to generate realistic waveforms of
multi-speakers, by alleviating the over-smoothing problem in the high frequency
band of the large footprint model. Our structure generates signals close to
ground-truth data without reducing the inference speed, by discriminating the
waveform and spectrogram during training. The model achieved the best mean
opinion score (MOS) in most scenarios using ground-truth mel-spectrogram as an
input. Especially, it showed superior performance in unseen domains with regard
of speaker, emotion, and language. Moreover, in a multi-speaker text-to-speech
scenario using mel-spectrogram generated by a transformer model, it synthesized
high-fidelity speech of 4.22 MOS. These results, achieved without external
domain information, highlight the potential of the proposed model as a
universal vocoder.
