## 3 Results
### Speech-Synthesis Systems  
Prior work has focused on speaker adaptation in conjunction with hidden-Markov-model (HMM)-based speech synthesis [17; 18; 19; 20; 21]. In one study, voices created by the HMM-based systems were generally regarded as more similar to the original speaker than the voice-converted unit-selection systems [20]. Researchers concluded that although the child-speech data had poor coverage of the phonetic/prosodic units of the language, an inconsistent reading style and imperfect recording conditions, it was feasible to build child voices by using the HMM-based speech-synthesis framework [20]. Similarly, the HMM-prototype voice created in another study had challenges related to intonation and pronunciation, noise levels, naturalness and volume levels [5], but the voice created was regarded to be around seven years old, which suggests the method can build child voices. [PERSON], [PERSON], and [PERSON] [53] recently attempted to synthesise emotional children's speech by comparing three models (1. HMM model, 2. conditional GAN model, 3. current model CycleGAN). It was clear that the emotional-classification performance of the CycleGAN model was the best, and the accuracy of the HMM-model was the lowest. It appears that although the HMM may be a feasible method for child-speech synthesis, findings suggest that the naturalness of the synthetic speech may be compromised. Modern synthesis methods, specifically related to deep learning, have had more success at improving the quality of child-speech-synthesis models. Traditionally, researchers have focused on Gaussian mixture models-hidden Markov models (GMM-HMM) for synthetic child-speech development, although more recently, there has been consensus in the community that deep neural networks (DNNs) are suitable for child-speech synthesis [24; 25; 26; 27; 28; 29; 30; 31; 32], and results show that DNNs have outperformed the older Gaussian mixture models (GMMs) [37; 54]. According to [PERSON] and [PERSON] [37], the success of DNNs is due to their ability to use large amounts of training data. Additionally, human speech is filled with non-linearity, and DNNs are able to better approximate the non-linear functions that are needed to model speech [37]. [PERSON] and [PERSON] have gone so far as to say that DNN acoustic models work better than GMMs, even when GMMs are trained with approximately eight times more data [54]. Both [PERSON] [30] and [PERSON] and [PERSON] [29] used a hybrid DNN-HMM-based automatic-speech-recognition system. They had approximately 10 h of Italian child speech, and results showed improvements over the traditional GMM-based systems. In addition, using a combination of adult and child training data in the DNN-based models resulted in improved results [24; 25; 26]. In particular, combining child speech with adult female speech has been proven to be advantageous [25; 55]. As the length of an adult female vocal tract is closer to the length of a child's vocal tract in comparison to that of males, manipulating an adult female voice into a child's voice has often been more successful [25]. In fact, the child voice that was adapted from an average-male-voice model experienced significantly larger distortion than that adapted from the average-female-voice model [55]. Similarly, a model trained on a male voice resulted in a less naturalistic child voice [47; 56]. Additionally, in order to improve the acoustic mismatch that often occurs as a result of using adult training data on child models, pitch normalisation has been introduced. It has been shown that significant improvement can be obtained with the maximum-likelihood-based explicit pitch normalisation of children's speech [57; 58; 59; 60; 61; 62].