Editing Hidden Markov Models in Speech Synthesis (section)

==== Improved Accuracy of Time Series Prediction ====
# Enhanced Training Strategies: Utilizing enhanced training strategies, such as reinforcement learning, to optimize the prediction accuracy of the time series, resulting in more natural rhythm and fluency in synthetic speech. [Ping et al., 2018]<ref>Ping, W., Peng, B., & Yang, X. (2018). [https://arxiv.org/abs/1802.08759 Deep voice 3: 2000-speaker neural text-to-speech]. In Advances in Neural Information Processing Systems (pp. 4967-4977).</ref>
# Model Fusion: Fusing HMM with other models, such as neural networks, to achieve higher timing modeling accuracy and prediction ability, resulting in smoother and more natural synthetic speech.  [Zen et al., 2009]<ref>Zen, H., Tokuda, K., Masuko, T., Kobayashi, T., & Kitamura, T. (2009). [https://ieeexplore.ieee.org/document/4783645 HMM-based speech synthesis integrated with speaker adaptation and its evaluation]. IEEE Transactions on Audio, Speech, and Language Processing, 17(2), 363-376.</ref>