  Most neural vocoders employ band-limited mel-spectrograms to generate
waveforms. If full-band spectral features are used as the input, the vocoder
can be provided with as much acoustic information as possible. However, in some
models employing full-band mel-spectrograms, an over-smoothing problem occurs
as part of which non-sharp spectrograms are generated. To address this problem,
we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms
in real time. Inspired by works in the field of voice activity detection, we
added a multi-resolution spectrogram discriminator that employs multiple linear
spectrogram magnitudes computed using various parameter sets. Using full-band
mel-spectrograms as input, we expect to generate high-resolution signals by
adding a discriminator that employs spectrograms of multiple resolutions as the
input. In an evaluation on a dataset containing information on hundreds of
speakers, UnivNet obtained the best objective and subjective results among
competing models for both seen and unseen speakers. These results, including
the best subjective score for text-to-speech, demonstrate the potential for
fast adaptation to new speakers without a need for training from scratch.
