Editing Advancements in Neural Network-Based TTS (2000s) (section)

== LLM Review ==
We asked ChatGPT to check the article for spelling and stylistic errors and, if necessary, rewrite certain phrases in a more scientific style, using the prompt "Act as a professor of speech science and review this wiki page, offering advice on how to make it coherent, logical and highlight area for improvement". We explained every acronyms that ChatGPT suggested to us to do, and unified our reference formant, made the text more coherent, informative. Some of the advice we didn't pick up, such as Structural Improvements, we don't think it is wise to seperate "Key Innovations" and "Articulatory Features-Based TTS",  The page now features a second version verified by ChatGPT. As a result, we received the following review:

The provided wiki page on "Advancements in Neural Network-Based TTS (2000s)" contains a lot of valuable information about the history, innovations, impact, and future research in the field of neural network-based text-to-speech (TTS). Overall, the content is informative, but there are a few areas where improvements can be made to enhance its clarity and organization. Here are some suggestions:

* Structural Improvements: Consider breaking down the content into subsections with clear headings for better navigation. For example, separate the "Key Innovations" section from the "Articulatory Features-Based TTS" section to improve the page's overall structure.
* Consistent Terminology: Ensure consistent use of terminology and acronyms throughout the page. For example, it would be beneficial to explain or provide links for less common acronyms such as "MOS."
* Citations: Provide clear citations for the sources of the information, especially when referencing research papers and specific models. This adds credibility to the content.
* Articulatory Features-Based TTS: Expand the section on "Articulatory Features-Based TTS" to include a brief explanation of what articulatory features are and why they are important in speech synthesis. This will help readers who may not be familiar with the concept.
* Visual Aids: Consider including visual aids like diagrams or flowcharts to help illustrate complex concepts, such as the structure of the WaveNet model or the operation of Tacotron.
* Prosody Modeling: Expand on the importance of prosody modeling, providing examples of how it impacts speech synthesis. Explain how neural network-based models have improved prosody modeling.
* End-to-End Systems: Highlight the advantages and disadvantages of end-to-end systems more explicitly. Discuss cases where they excel and where they may have limitations.
* Generative Modeling: Provide more context on the role of generative models in speech synthesis and how they compare to traditional methods like concatenative and parametric synthesis.
* Multi-Modal Speech Synthesis: Include examples or use cases of multi-modal speech synthesis to make it more tangible for readers. Explain how it benefits individuals with hearing impairments, for instance.
* Efficient Speech Synthesis: Elaborate on the challenges of achieving energy-efficient training and how it relates to the use of neural network models in TTS.
* Cross-Lingual and Multi-Lingual Speech Synthesis: Offer more insights into the challenges of handling different languages, such as phonetic and syntactic variations, without interfering with each other.
* Conclusion and Future Research: Summarize the main takeaways from the article and reiterate the importance of neural network-based TTS advancements in the broader context of AI and speech technology.
* References: Include a section for references, listing the sources and publications that were cited throughout the article.

By addressing these points, the wiki page can become more coherent, informative, and user-friendly for readers interested in the field of neural network-based TTS.