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

=== 7. Evolving Neural Architectures and [https://wiki.voice-technology.nl/index.php/Development_of_End-to-End_Models End-to-End] Approaches ===
In recent years, the field of AI-driven TTS has seen remarkable progress. Neural network architectures, particularly Transformers and their variants, have revolutionized TTS research by enhancing parallelization, enabling real-time, and high-quality TTS. These architectures, featuring attention mechanisms and positional embeddings, have become standard for capturing context and improving synthesis quality. In this context, End-to-End Approaches have made significant strides since 2020, streamlining the TTS process by utilizing powerful neural networks to directly transform text into speech waveforms. <ref>Tu, T., Chen, Y.-J., Yeh, C., & Lee, H. (2019). ''End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning'' (arXiv:1904.06508). arXiv. <nowiki>http://arxiv.org/abs/1904.06508</nowiki></ref>This development has yielded voices that are more human-like and of higher quality, with enhanced customization and personalization capabilities. However, challenges such as data availability and fine-tuning for less common languages persist, yet End-to-End TTS continues to find application in real-time scenarios and personalized voice synthesis.<ref name=":2" />