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.

我们提出了 Universal MelGAN，它是一种合成高保真语音的声码器，可以在多个领域中使用，并通过添加多分辨率频谱图鉴别器来解决大型模型高频带过度平滑的问题，最终生成高度逼真的语音信号。

通用 MelGAN: 多领域高保真波形生成的强力神经声码器

Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform  Generation in Multiple Domains

In recent years, neural vocoders have surpassed classical speech generation
approaches in naturalness and perceptual quality of the synthesized speech.
Computationally heavy models like WaveNet and WaveGlow achieve best results,
while lightweight GAN models, e.g. MelGAN and Parallel WaveGAN, remain inferior
in terms of perceptual quality. We therefore propose StyleMelGAN, a lightweight
neural vocoder allowing synthesis of high-fidelity speech with low
computational complexity. StyleMelGAN employs temporal adaptive normalization
to style a low-dimensional noise vector with the acoustic features of the
target speech. For efficient training, multiple random-window discriminators
adversarially evaluate the speech signal analyzed by a filter bank, with
regularization provided by a multi-scale spectral reconstruction loss. The
highly parallelizable speech generation is several times faster than real-time
on CPUs and GPUs. MUSHRA and P.800 listening tests show that StyleMelGAN
outperforms prior neural vocoders in copy-synthesis and Text-to-Speech
scenarios.

提出 StyleMelGAN 算法，实现高保真度语音合成，并解决了低计算复杂度的问题。