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

== Historical Context ==
The history of neural network-based text-to-speech (TTS) can be traced back to the early days of artificial intelligence research. In the 1980s, researchers began to explore the use of neural networks to model the human speech production process. In the 1990s, [[Hidden Markov Models]] were introduced to TTS, which brought significant improvements in speech synthesis. HMM-based systems allowed for better control of speech characteristics and were widely adopted for several years.

In early 2000s, researchers started exploring the use of deep neural networks (DNNs) for speech synthesis''.'' However, it wasn’t until the introduction of generative adversarial networks (GANs) and autoregressive models that the quality of synthesized speech improved significantly. In recent years, the development of deep learning and artificial intelligence has led to a surge in research on neural network-based TTS.<ref>Xu Tan∗, Tao Qin, Frank Soong, Tie-Yan Liu. "A Survey on Neural Speech Synthesis" arXiv:2106.15561 (2021)</ref>

One of the key breakthroughs in neural network-based TTS came in 2006 with the introduction of the WaveNet model by Google AI. WaveNet was the first neural network-based TTS system to generate high-quality speech waveforms directly from text, without the need for intermediate representations such as phonemes or mel spectrograms.<ref>Dario Amodei, Dario, Aidan N. Gomez, et al. "WaveNet: A Generative Model for Raw Audio." arXiv preprint arXiv:1609.03499 (2016).</ref> This led to a significant improvement in the naturalness and expressiveness of synthesized speech.