Editing Hidden Markov Models in Speech Synthesis (section)

=== End-to-End Learning ===
Adopting an end-to-end learning approach, mapping input text directly to acoustic features, avoiding manually designed feature extraction steps, simplifying the system, and improving performance. Key innovations in end-to-end learning in the domain of speech synthesis with HMMs primarily focus on the direct mapping from input text to synthesized speech in end-to-end model design and training. This approach bypasses the cumbersome intermediate steps in traditional speech synthesis systems and directly maps text to corresponding acoustic features. These innovations have made end-to-end learning a significant research direction in the field of speech synthesis, greatly simplifying the system flow while improving the quality and naturalness of synthesized speech.

==== [[Development of End-to-End Models|End-to-End Model]] Design ====
# Text-to-Speech End-to-End Model: Designing an integrated neural network model that takes text as input and outputs corresponding acoustic features, achieving direct mapping from text to acoustic features. [Wang et al., 2017]<ref name=":1">Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., ... & Auli, M. (2017). [https://arxiv.org/abs/1703.10135 Tacotron: Towards end-to-end speech synthesis]. arXiv preprint arXiv:1703.10135.</ref>
# Self-Attention Mechanism: Introducing a self-attention mechanism to capture long-distance dependencies in input text, enhancing the model's ability to handle longer texts. [Shen et al., 2018]<ref>Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., ... & Wu, Y. (2018). [https://arxiv.org/abs/1804.10216 Natural TTS synthesis by conditioning WaveNet on Mel spectrogram predictions]. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4779-4783).</ref>

==== Joint Training ====
# Joint Training of Text and Acoustic Features: Training an end-to-end model to jointly predict text and acoustic features for improved performance and more natural speech synthesis results. [Wang et al., 2017]<ref name=":1" />
# Reinforcement Learning Optimization: Leveraging reinforcement learning to optimize the model, enabling it to generate acoustic feature sequences that align better with the fluency of natural speech. [Ping et al., 2017]<ref>Ping, W., Peng, B., & Yang, X. (2017). [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>