Editing State-of-the-art (section)

===== Huang, R., Huang, J., Yang, D., Ren, Y., Liu, L., Li, M., ... & Zhao, Z. (2023, July). Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models. In ''International Conference on Machine Learning'' (pp. 13916-13932). PMLR. =====
* Summary: The article introduces "Make-An-Audio," a system utilizing a prompt-enhanced diffusion model for TTS generation, aiming to improve the naturalness and expressiveness of synthesized audio.
* RQ: How does the model improve the naturalness of TTS?
* Hypothesis: By introducing pseudo prompt enhancement and spectrogram autoencoders, the model can effectively utilize unsupervised language-free data and higher-level semantic understanding to enhance the naturalness and expressiveness of speech synthesis.
* Conclusion: "Make-An-Audio" successfully enhances the naturalness and expressiveness of speech synthesis, achieving state-of-the-art performance in evaluations.
* Critical observations: The performance of "Make-An-Audio" is still partly dependent on extensive data and complex model training. In addition, there is still space for improvement in expressing the emotions and rhythms of human conversations.
* Relevance: The "Make-An-Audio" system presented in the paper offers an effective solution to the limitations in naturalness and expressiveness currently faced by TTS