Editing Vocoder Development (section)

== Future Research ==
Despite the significant advancements in technology and the development of new speech synthesis systems, ranging from [[DECtalk Speech Synthesizer (1984)|DECtalk]] in the 1980s to the cutting-edge [[Advancements in AI TTS (2020s)|AI models]] of today, vocoders continue to play a key role in various applications. Today, vocoders are integral components of state-of-the-art speech synthesis systems, including WORLD<ref name=":3" />, designed specifically for real-time applications, and BigVGAN<ref>Lee, S. G., Ping, W., Ginsburg, B., Catanzaro, B., & Yoon, S. (2022). Bigvgan: A universal neural vocoder with large-scale training. ''arXiv preprint arXiv:2206.04658''.</ref> , which uses the power of generative adversarial networks (GANs<ref>Rocca, J. (2019). Understanding generative adversarial networks (gans). ''Medium'', ''7'', 20.</ref>). A majority of contemporary vocoders rely on neural networks for their operation, but improvements could be made in this direction too. Additionally, the integration of generative AI holds the promise of further enhancing the quality of vocoder-synthesized voices.

Furthermore, vocoders are largely used in the field of music production, although we have yet to achieve the creation of truly authentic singing voices through them. The potential to explore and refine the use of vocoders in this context offers a pathway to a broader advancement in speech synthesis technology, since the effort of synthesizing a singing voice may lead us to a more fluent and smooth speaking voice.

Additionally, vocoders are currently deployed to address other challenges in voice technology. Vocoder-synthesized voices serve as tools in training noise-robust recognition models<ref>Zheng, N., Shi, Y., Kang, Y., & Meng, Q. (2021, June). A noise-robust signal processing strategy for cochlear implants using neural networks. In ''ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'' (pp. 8343-8347). IEEE.</ref> and detect fake audio<ref>Yan, X., Yi, J., Tao, J., Wang, C., Ma, H., Wang, T., ... & Fu, R. (2022, October). An initial investigation for detecting vocoder fingerprints of fake audio. In ''Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia'' (pp. 61-68).</ref>. The former represents a useful development not only for the industry of speech recognition but also an improvement for medical applications of speech recognition and processing. The latter allows us to take steps forward in the direction of cybersecurity and data protection<ref>Lim, S. Y., Chae, D. K., & Lee, S. C. (2022). Detecting deepfake voice using explainable deep learning techniques. ''Applied Sciences'', ''12''(8), 3926.</ref> since it could enable us to automatically detect identity theft attempts and privacy violations.