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

===== c. Personalization =====
Personalization is expected to be another major aspect of future speech synthesis systems. Both the expression and the perception of emotion show individualistic effects, which are currently underexploited in the speech synthesis field. Future approaches can benefit a lot from adopting a similar mindset and adapt the production of emotional speech to a style that fits both the speaker and the listener. Such an interpersonal adaptation effect is also seen in human conversations and is a necessary step to foster communication<ref name=":1" />.

Specifically, '''child-speech-synthesis''' is one promising research area. Due to the difficulties in collecting children speech data and understanding children speech, synthesizing children speech has always been challenging. In recent years, neural-network-based TTS systems have been gaining popularity. For instance, Hasija, Kadyan, and Guleria<ref>Hasija, T.; Kadyan, V.; Guleria, K. Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron. In Proceedings of the International Conference on Mathematics and Artificial Intelligence (ICMAI 2021), Chengdu, China, 19–21 March 2021.[https://iopscience.iop.org/article/10.1088/1742-6596/1950/1/012044]</ref> used Tacotron for the development of children’s synthetic speech.  However, the problem of lacking data for children's speech still persists. For future developments, researchers need to define better acoustic features for children's speech. Moreover, pronunciation modelling is required<ref>Terblanche, C., Harty, M., Pascoe, M., & Tucker, B. V. (2022). A Situational Analysis of Current Speech-Synthesis Systems for Child Voices: A Scoping Review of Qualitative and Quantitative Evidence. ''Applied Sciences'', ''12''(11), 5623. <nowiki>https://doi.org/10.3390/</nowiki>[https://www.mdpi.com/2076-3417/12/11/5623 app12115623]</ref>.