This has repercussions for other areas of their life, such as accessing healthcare, participating in family and community activities, as well as engaging in future employment. As a result, their potential is often underestimated. This may result in fewer instances of intentional communication, minimal language and literacy development, and poor opportunities for socialisation. We encourage future research surrounding individualised synthetic speech for children with CCN, with special attention to children who make use of low-resource languages.Ĭommunication is essential to learning without access to functional communication, children with CCN are often restricted from participating in the classroom. 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. (4) Conclusions: Adapting adult speech has proven successful in child-speech synthesis. To account for this, researchers have most often attempted to adapt adult-speech models, using a variety of different adaptation techniques. Child speech often presents with acoustic variability and articulatory errors. Based on the reviewed studies, relative to adult-speech synthesis, developing child-speech synthesis is notably more challenging. They were discussed based on the languages used, the speech-synthesis systems and/or methods used, the speech data used, the intelligibility of the speech and the ages of the voices. (2) Method: The included studies were those that were (1) published between 20 and (2) included child participants or voices of children aged between 2–16 years old. This scoping review summarises the evidence base related to developing synthesised speech for children. (1) Background: Speech synthesis has customarily focused on adult speech, but with the rapid development of speech-synthesis technology, it is now possible to create child voices with a limited amount of child-speech data.
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