  Unlike human speakers, typical text-to-speech (TTS) systems are unable to
produce multiple distinct renditions of a given sentence. This has previously
been addressed by adding explicit external control. In contrast, generative
models are able to capture a distribution over multiple renditions and thus
produce varied renditions using sampling. Typical neural TTS models learn the
average of the data because they minimise mean squared error. In the context of
prosody, taking the average produces flatter, more boring speech: an "average
prosody". A generative model that can synthesise multiple prosodies will, by
design, not model average prosody. We use variational autoencoders (VAEs) which
explicitly place the most "average" data close to the mean of the Gaussian
prior. We propose that by moving towards the tails of the prior distribution,
the model will transition towards generating more idiosyncratic, varied
renditions. Focusing here on intonation, we investigate the trade-off between
naturalness and intonation variation and find that typical acoustic models can
either be natural, or varied, but not both. However, sampling from the tails of
the VAE prior produces much more varied intonation than the traditional
approaches, whilst maintaining the same level of naturalness.
