## 5 Conclusions  
This scoping review addressed the current state of knowledge regarding the development of child-speech synthesis based on research conducted over the last 15 years and reveals potential directions for future research. However, the possibility of not including all relevant articles must be recognised. Selecting other databases may have identified additional relevant studies. Additionally, relevant articles may have used terms other than speech-generating device or voice output communication aid. Finally, we did not critically appraise individual sources of evidence, as the focus of this review was to identify available evidence rather than to evaluate it. This scoping review did not evaluate specific speech-synthesis systems or judge the utility of these systems for children with CCN. However, what emerges from the evidence is that speech-synthesis technology has improved remarkably over the last 15 years. In fact, in the last few years, it has become possible to create intelligible and natural-sounding synthetic speech that has the potential to mislead listeners to thinking that they are listening to authentic speech. Many speech-synthesis and voice-conversion technologies have become easily accessible through open-source software. However, based on the studies reviewed, relative to adult-speech synthesis, developing child-speech synthesis, particularly for young children, is notably more challenging for researchers. Child speech often presents with acoustic variability, disfluencies, and articulatory errors. In addition to this, it is often challenging to collect child speech due to children's short attention spans, limited reading skills and the diverse recording environments. To account for these challenges, numerous researchers have attempted to adapt adult-speech models, using a variety of different adaptation techniques. In most cases, adult-speech data are used in combination with a small amount of child-speech data to create a synthetic child-like voice. Adapting adult speech has proven successful in child-speech synthesis and it appears that the resulting quality can be improved by training a large amount of pre-selected speech data, aided by a neural-network classifier, to better match the children's speech. For children who are unable to communicate using their natural speech, speech synthesis could provide a viable means of communication. The selected synthetic voice used in the speech-generating device is therefore likely to become an extension of themselves. With that in mind, we propose that the synthetic voice be individualised to best represent the child's vocal identity, with regards to at least their language, sex, and age. With an individualised synthetic voice, children with complex communication needs could potentially increase their intentional communication skills, participation, language, and literacy skills in a classroom setting. As multiple children in one classroom may need a synthetic voice, having an individualised voice would benefit them greatly as teachers would be able to differentiate speakers in class, there may be greater technology-adoption rates and an increased level of socialisation between children. Future research could focus on developing a system that is acceptable to the child and improves its performance over time based on continued use by the child. As language is a large part of an individual's vocal identity, the language selected for the device is another important element to consider. Many of the speech-synthesis systemsare usually designed for major languages, such as English, but are limited for low-resource languages. Promisingly, it was discovered that if there are enough training data available, either collected in the typical fashion or atypically, through crowdsourcing, or by combining language-similar corpora (borrowing speech data from other corpora that fall within the same language family), one should be able to create natural and intelligible synthetic speech for children in any language.