Editing Hidden Markov Models in Speech Synthesis (section)

==== Integration of Recurrent Neural Network and HMM (RNN-HMM) ====
# RNN for Timing Modeling: Integrating recurrent neural networks (RNN) to model the timing of HMM, better capturing long-term information and improving fluency and naturalness of synthetic speech. [Graves et al., 2013]<ref>Graves, A., Mohamed, A. R., Hinton, G. (2013). [https://www.cs.toronto.edu/~hinton/absps/RNN13.pdf Speech recognition with deep recurrent neural networks]. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference (pp. 6645-6649).</ref>
# Long-term Sequence Modeling: Introducing long-term memory networks (LSTM) as a variant of RNN to address the problem of gradient vanishing in long-time series modeling and improve the time series modeling ability. [Hochreiter and Schmidhuber, 1997]<ref>Hochreiter, S., & Schmidhuber, J. (1997). [https://www.mitpressjournals.org/doi/10.1162/neco.1997.9.8.1735 Long short-term memory]. Neural computation, 9(8), 1735-1780.</ref>