Editing Hidden Markov Models in Speech Synthesis (section)

==== [[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>