  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.
