Editing Hidden Markov Models in Speech Synthesis (section)

==== Long-term Sequential Modeling ====
# Methods for Long-time Sequence Modeling: Addressing long-time sequence problems by introducing methods to capture long-time sequence features more accurately, enhancing the naturalness of synthetic speech. [Tjandra et al., 2017]<ref>Tjandra, A., Cohn, T., Schuster, M., & Schröder, S. (2017). [https://arxiv.org/abs/1703.10135 Listening while speaking: Speech recognition during speech production is modulated by visually perceived speech rate.] PloS one, 12(3), e0173612.</ref>
# Long-term Sequence Memory Mechanisms: Implementing long-term sequence memory mechanisms like gated recurrent units (GRU) or long-short-term memory networks (LSTM) to effectively capture long-term sequence information and improve timing modeling ability. [Chung et al., 2015]<ref>Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2015). [https://arxiv.org/abs/1412.3555 Gated feedback recurrent neural networks]. In Proceedings of the 32nd International Conference on International Conference on Machine Learning (pp. 2067-2075).</ref>