# Using generative modelling to produce varied intonation for speech synthesis

## TL;DR

This paper demonstrates that variational autoencoders can generate diverse intonations in speech synthesis, balancing naturalness and variation better than traditional models by sampling from the tails of the prior distribution.

## Contribution

It introduces a VAE-based approach to produce varied speech intonation, moving beyond average prosody and enabling more expressive TTS systems.

## Key findings

- Sampling from the tails of the VAE prior increases intonation variation.
- The approach maintains naturalness while enhancing expressiveness.
- Traditional models trade off naturalness for variation, unlike the proposed method.

## Abstract

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.