  Recently, neural vocoders have been widely used in speech synthesis tasks,
including text-to-speech and voice conversion. However, when encountering data
distribution mismatch between training and inference, neural vocoders trained
on real data often degrade in voice quality for unseen scenarios. In this
paper, we train four common neural vocoders, including WaveNet, WaveRNN,
FFTNet, Parallel WaveGAN alternately on five different datasets. To study the
robustness of neural vocoders, we evaluate the models using acoustic features
from seen/unseen speakers, seen/unseen languages, a text-to-speech model, and a
voice conversion model. We found out that the speaker variety is much more
important for achieving a universal vocoder than the language. Through our
experiments, we show that WaveNet and WaveRNN are more suitable for
text-to-speech models, while Parallel WaveGAN is more suitable for voice
conversion applications. Great amount of subjective MOS results in naturalness
for all vocoders are presented for future studies.
