There is an older paper [0] and demo from [1] Alex Graves that inspired a ton of work around handwriting, and then speech. Previous work from Jose Sotelo et. al. (including me) called char2wav [2] is a close neighbor to Graves' approach, though he (Graves) never published the approach for speech so we don't really know. Google's recent Tacotron paper [3] is also a relative to these approaches.
WaveNet certainly changed the game in many ways, but approaches to TTS using RNNs have different roots. WaveNet and friends (incl. DeepVoice and NPSS linked elsewhere in this thread) are largely focused on audio modeling, and generally use something closely related to the "classic" TTS pipeline for text in the frontend. The audio modeling results are stellar, and really blew me away personally - basically changing my perspective on what is possible in audio modeling overnight.
RNN models try to tackle the whole problem (text + audio modeling) at once, though currently (all?) RNN and attention style models need intermediate / high level hints or pretraining from things like vocoder representations or spectrograms, versus WaveNet's approach using the waveform directly. So they are complimentary in many ways, and I am sure we will see people trying to combine them soon - char2wav has this flavor by using SampleRNN, our lab's take on raw waveform generation though we are still working on the fully end-to-end from scratch training, the inference path is truly end-to-end. Though there are still many details to work out as far as output quality, it seems possible that this will be a productive approach (though I am quite biased).
We see similar directions in neural machine translation (NMT) moving from word level representations to word parts or characters directly - one of the big reasons deep learning has come so far, so fast is that a lot of techniques from other subfields can be utilized for new domains, and I think there is a lot more fertile ground for crossover in both directions.
Heiga Zen has a great overview talk about how speech synthesis, as a field, overlaps between different approaches and factorizations [4]. His work on parametric synthesis and TTS generally has laid the foundation for a lot of recent advances, and he was also a co-author on WaveNet!
[0] https://arxiv.org/abs/1308.0850
[1] https://www.youtube.com/watch?v=-yX1SYeDHbg&t=38m0s
[2] http://josesotelo.com/speechsynthesis/
[3] https://google.github.io/tacotron/
[4] https://www.youtube.com/watch?v=nsrSrYtKkT8