## 1 Introduction
[PERSON] [9] suggested that people interact differently with machines, and if the perception is that they are addressing the communication aid rather than the human user, their interactional style will act as a further obstacle to the user's ability to form desirable social relationships with conversational partners. It appears listeners prefer a SGD to have a voice that is consistent with the characteristics of the person who is using it [2]. _Pleasantness_ focuses on the pleasantness or agreeableness of the synthetic voice [10]. Although having a high quality and natural-sounding voice reduces listener fatigue and therefore contributes to positive attitudes towards the SGD by both the user and their conversational partners [9], it is also noteworthy to mention that listeners with greater exposure to synthesised speech, through practice effects, become better at analysing the acoustic-phonetic information in the signal, making better use of the acoustic cues in the synthesised speech, which results in increased intelligibility [11]. A decade ago, [PERSON] et al. [3] (p. 1) outlined that \"it is not easy, and certainly not cost effective, for manufacturers to create personalized synthetic voices\". However, technology has sufficiently matured to realistically emulate individual speakers' voices electronically [12]. Researchers have shown that the results of new speech-synthesis systems are good enough to mislead listeners to thinking that they are listening to authentic voices when they are in fact synthetic [13; 14; 15]. Additionally, many speech-synthesis and voice-conversion technologies have become easily accessible through open-source software, and this technology is advancing quickly. Thus, it is not improbable to consider that freely available software be used to assist individuals with CCN. However, these speech-synthesis systems are currently available for various major languages, such as English and other European languages, but are limited for South Africa's indigenous languages [10]. Accents also reflect the market size and English speech is therefore often US-accented [3]. South Africa has 11 official languages, most of which could be considered low-resource languages [16]. Thus, South Africa's rich linguistic and social diversity is not well represented in open-source technology. This is also true for commercially available SGDs, where the speech output is limited. Voices representing a _male_, _female_ and in some cases, a _child_ are available on the devices, but unfortunately, these same voices are used for numerous individuals and may only represent an individual's identity in a handful of cases [4]. Moreover, speech-synthesis technology has customarily focused on adult rather than child speech. Although several methods have been introduced, for a long time, researchers used speaker adaptation in conjunction with hidden-Markov-model (HMM)-based speech synthesis [17; 18; 19; 20; 21]. With HMM-based speech synthesis, statistical acoustic models for spectral, excitation, and duration features can be precisely adapted from an average-voice model (derived from other speakers) or a background model (derived from one speaker) using a small amount of speech data from the target speaker [22]. Novel utterances are then created when models are concatenated (overlapping and adding the signals together), generating the most suitable sequence of feature vectors from the concatenated model for which the speech waveform is synthesised [22]. Although HMM-based speech synthesis has been used for child-speech synthesis [17; 19; 20; 23], the consensus in the community is that deep neural networks (DNNs) are more suitable for child-speech synthesis [24; 25; 26; 27; 28; 29; 30; 31; 32]. However, there are several difficulties associated with collecting appropriate child-speech data. [PERSON] et al.