Explain to a layperson why text-to-speech (TTS) has made little progress?
Text-to-speech suffers from problems similar to the drivers of "uncanny valley" effects when viewing faces of humanoid robots and computer renderings of faces. Our ability to distinguish depth of meaning, emotional content and other subtle cues makes humans very sensitive to small details in audio containing spoken language. Note this is not quite the same as claiming text-to-speech actually has an "uncanny valley"; there doesn't appear to be much analysis of that claim either way around.

Existing text-to-speech systems have two basic generative processes: Concatenative models are essentially databases of phoneme samples that the system strings together. These suffer from lack of flexibility. Parametric models attempt to represent sound generation at a lower level to improve on this, but it is quite a challenge to create a model that includes all the fine detail that we notice. For instance, we notice many non-verbal cues including imperfections such as mouth noise, breathing etc.

There has been recent progress in this area by researchers running a very detailed generative model - one that literally generates sound, sample by sample, after consuming a large amount of training data.

This report on WaveNet by DeepMind team explains the different synthesis techniques used to date and shows off the capabilities of the new approach.

The process creates state-of-the-art results, and it is easy to hear the improvement from examples on the site. However, it is too computationally intensive to be used in real-time systems yet. Give it a few years to be refined, and it could be the basis of far better text-to-speech systems.



The above written in January 2017. In the intervening time (it is now October 2017) the DeepMind team have been working on the efficiency of their model, and it is now much faster and sounds even better. This is close to becoming a solved problem, albeit with some proprietary ownership. Give it a little while longer though and this breakthrough will allow real-time and natural-sounding parametric voice models in many applications.