  This paper explores the potential universality of neural vocoders. We train a
WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is
shown to be capable of generating speech of consistently good quality (98%
relative mean MUSHRA when compared to natural speech) regardless of whether the
input spectrogram comes from a speaker or style seen during training or from an
out-of-domain scenario when the recording conditions are studio-quality. When
the recordings show significant changes in quality, or when moving towards
non-speech vocalizations or singing, the vocoder still significantly
outperforms speaker-dependent vocoders, but operates at a lower average
relative MUSHRA of 75%. These results are shown to be consistent across
languages, regardless of them being seen during training (e.g. English or
Japanese) or unseen (e.g. Wolof, Swahili, Ahmaric).
