The simplest example is the RNN-RBM (restricted boltzmann machine) from the Theano deep learning tutorials [1]. There are follow-up papers by Kratarth Goel extending this to LSTM-DBN, and RNN-NADE (neural autoregressive density estimator) has also been explored in this space. The results for MIDI are pretty good.

Ales Graves [2] has done a huge amount of work in sequence modeling for classification and generation. His work with LSTM-GMM for online handwriting modeling can also be applied to speech features (vocoded speech), and seems to work well. No official publication to date on the speech experiments themselves, but the procedure is well documented in his paper [3]

The most recent thing is our paper "A Recurrent Latent Variable Model for Sequential Data" [4]. It operates on raw timeseries (!) even though all intuition would say that an RNN shouldn't handle this well. Adding a recurrent prior seems to preserve style in the handwriting generations at least - I may try on this "Let It Go!" task and see what happens! We are also exploring conditional controls and higher-level speech features, which should result in samples as good or better than LSTM-GMM.

[1] http://deeplearning.net/tutorial/rnnrbm.html#rnnrbm

[2] https://www.youtube.com/watch?v=-yX1SYeDHbg#t=36m50s

[3] http://arxiv.org/abs/1308.0850

[4] http://arxiv.org/abs/1506.02216