Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures with respect to text-to-speech syntheses and for all perceptual metrics, their performances are not distinguishable from one another, which supports our hypothesis.
@article{arxiv.2103.05236,
title = {GAN Vocoder: Multi-Resolution Discriminator Is All You Need},
author = {Jaeseong You and Dalhyun Kim and Gyuhyeon Nam and Geumbyeol Hwang and Gyeongsu Chae},
journal= {arXiv preprint arXiv:2103.05236},
year = {2021}
}