## 4 Discussion
### Speech-Synthesis Systems  
When considering building child-speech-synthesis systems, one needs to ask several questions: (a) What kind of average-voice model (gender-dependent or gender-independent) is the most appropriate initial model from which adaptation will occur? (b) Are there enough training data for the initial model? (c) Are there enough adaptation data to improve adaptation performance? (d) Should some training/adaptation data be excluded from the model? If so, how much? (e) What kind of adaptation techniques (VTLN, SAT etc.) will be utilised? If so, would it be conducted on the adult speech, the child speech or both? (f) What kinds of transform functions are appropriate?  
It appears that there are numerous synthesis methods available, each with their own advantages and disadvantages. As mentioned previously, neural-network-based text-to-speech systems have been gaining popularity in recent years. This speech-generation method is based on deep learning and it can mine the potential correspondence from multiple corpora and automatically learn the dependence from the source sequence to the target sequence [53]. A neural-network-based acoustic model predicts a sequence of acoustic features, including the mel-cepstral coefficients (MCCs), interpolated fundamental frequency and voicing flags. Once completed, a vocoder analyses and converts these MCCs and the fundamental frequency into a waveform, which forms a synthesised voice [14; 43]. According to [PERSON] et al. [14] and [PERSON], [PERSON], and [PERSON] [15], new neural-network-based text-to-speech systems, such as Tacotron 2, produce synthetic speech that has a perceptually high level of naturalness and good similarity to adult target speakers. Tacotron was also used by [PERSON], [PERSON], and [PERSON] [43] for the development of children's synthetic speech. DNNs are therefore suitable for child-speech synthesis [24; 25; 26; 27; 28; 29; 30; 31; 32] and often outperform the older GMM models [37; 54]. However, it should be noted that a large amount of training data is not always freely available when developing child speech, particularly when one considers building child speech for low-resource languages. It appears that in order to account for this, researchers began using both adult and child speech for training in the DNN-based models, and this resulted in improved results.  
There are a number of techniques that have been introduced to improve child-speech synthesis. Firstly, researchers need to define better acoustic features for children's speech. The most commonly used features include mel-frequency cepstral coefficients (MFCCs), filterbank, and perceptual-linear-prediction (PLP) coefficients [25]. Typically, MFCCs achieve the best performance in GMM-based systems [59, 68] while mel-filterbank coefficients are often used in DNN-based systems [25]. Secondly, due to the differences in child-speech development, pronunciation modelling is required [68]. Thirdly, VTLN is often used to account for the differences in vocal-tract length between speakers [44, 50, 55, 75]. Fourthly, modal-adaptation techniques are often used, such as maximum a posterior (MAP) and maximum-likelihood linear regression (MLLR) [25, 49].