Editing Hidden Markov Models in Speech Synthesis (section)

=== Time Series Model Optimization ===
Optimizing the model's temporal properties to better capture the temporal variations in speech signals, especially in terms of coherence and fluency between different phonemes. Key innovations in temporal model optimization in the domain of speech synthesis with HMMs primarily focus on improving the model's ability to model temporal properties, handle long sequences, and enhance the model's temporal prediction accuracy. These innovations have significantly advanced HMM models in temporal modeling, laying the foundation for achieving more natural and fluent speech synthesis.

==== 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>

==== 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>