  We explore pretraining strategies including choice of base corpus with the
aim of choosing the best strategy for zero-shot multi-speaker end-to-end
synthesis. We also examine choice of neural vocoder for waveform synthesis, as
well as acoustic configurations used for mel spectrograms and final audio
output. We find that fine-tuning a multi-speaker model from found audiobook
data that has passed a simple quality threshold can improve naturalness and
similarity to unseen target speakers of synthetic speech. Additionally, we find
that listeners can discern between a 16kHz and 24kHz sampling rate, and that
WaveRNN produces output waveforms of a comparable quality to WaveNet, with a
faster inference time.
