MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Table 5: Mean Opinion Score of end to end speech synthesis. Evaluation protocol/details can be found in appendix B.
['Model', 'MOS', '95% CI']
Text2mel generates mel-spectrograms instead of vocoder frames, uses phonemes as the input representation and can be coupled with WaveGlow or MelGAN to invert the generated mel-spectrograms. We use this model since its sampler, trains faster and does not perform any mel-frequency clipping like Tacotron2. We use the open source implementations of Tacotron2 and WaveGlow provided by NVIDIA in the Pytorch Hub repository to generate the samples. When using WaveGlow, we use the Denoiser with strength 0.01 provided in the official repository to remove high frequency artifacts.