Editing Advancements in AI TTS (2020s) (section)

=== 1. Transfer Learning and Pretrained Models ===
One of the pivotal advancements in the 2020s has been the widespread adoption of transfer learning in TTS. Transfer learning allows knowledge learned from one or more base models to be transferred to other tasks. <ref>Fang, W., Chung, Y.-A., & Glass, J. (2019). ''Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models'' (arXiv:1906.07307). arXiv. <nowiki>http://arxiv.org/abs/1906.07307</nowiki></ref>For TTS, this can include knowledge learned from a general speech model and then applied to personalized voice synthesis. This helps expedite the training of personalized voice models as the model already possesses some general speech characteristics. <ref>Huang, W.-C., Hayashi, T., Wu, Y.-C., Kameoka, H., & Toda, T. (2019). ''Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining'' (arXiv:1912.06813). arXiv. <nowiki>http://arxiv.org/abs/1912.06813</nowiki></ref>Models like GPT-3 and BERT, initially designed for natural language processing, have been adapted for TTS tasks<ref>Dida, H. A., Chakravarthy, D. S. K., & Rabbi, F. (2023). ChatGPT and Big Data: Enhancing Text-to-Speech Conversion. ''Mesopotamian Journal of Big Data'', ''2023'', 33–37.</ref>. This approach has led to more efficient training and improved performance in TTS systems, with less need for extensive domain-specific data.