## 3 Results
### Child-Speech Data  
Due to the scarcity of child-speech data available for researchers, along with the typical articulatory errors, simplifications, hesitations, and disfluencies present in child speech, researchers have attempted to overcome these difficulties when developing child-speech synthesis, in a number of ways. [PERSON] and [PERSON] [28; 32] used a similar technique to [PERSON] et al. [27] to account for limited training data. [PERSON], [PERSON], and [PERSON] [43] decided to account for the scarcity of available child speech by combining two corpora, one synthesised and one clean (authentic), in order to generate high-quality synthetic child speech. Thereafter, a corpus with a greater quantity was used. Their results indicated that the merged-data corpus showed a reduced word-error rate of the automatic-speech-recognition system with a relative improvement of 9-12%. In a study by [PERSON], [PERSON], and [PERSON] [23], data were selected from an automatic-speech-recognition corpus to build child voices instead of using text-to-speech data. Speech recordings used for automatic-speech-recognition corpora are usually shorter and more spontaneous in comparison to the carefully articulated recorded speech used for text-to-speech development. The criteria considered for adaptation in their study [23] included: (1) clean data (with regards to transcription) and no mispronunciations or mistakes in the recordings, (2) data including mispronounced words, (3) number of words in the utterance, (4) rate of speech, and (5) maximum fundamental frequency [23]. The results showed that when comparing intelligibility, the word-error rates were not as close to voices that were built using speech data that were specifically recorded for speech-synthesis purposes, such as text-to-speech data. However, in terms of naturalness, if data were selected according to particular criteria, then automatic-speech-recognition data could be used to develop child voices that are comparable to text-to-speech voices [23]. In addition, many researchers have attempted to build child-speech models by adapting adult-speech models. This has been proven to be a viable method when there are limited speech data available [5; 18; 23; 24; 25; 26; 37; 44; 48]. In a study by [PERSON], [PERSON] and [PERSON] [18], a synthetic children's model was derived without child-speech data by using adult-speech data. While they assumed that the availability of child-speech data would have improved the resulting acoustic models, the approach was effective when child-speech data was not available [18]. According to a number of researchers [19; 37; 43], increasing the amount of training or adaptation data results in lower word-error rates. Thus, when creating child voices from adult-speech models, it appears that using more data yields better results, but data should not simply be selected blindly. Some researchers have recommended that one should select training speakers that are closer to the target speaker to train the initial models [23]. [PERSON] and [PERSON] [37] have concluded that any amount of child data is helpful for adaptation. In their experiments, even as little as 35 min of child-adaptation data were found to yield relative improvements of up to 9.1% over the adult model [37]. However, researchers still need to carefully select the child-speech data, as the typical articulatory errors and disfluencies that are commonly present in child speech will also appear in the synthetic speech without careful selection. This was the case in [PERSON] and [PERSON]'s study [19], and although the output sounded like child-read speech, it was not fluently spoken, which is not ideal for a SGD. Of course, if there are enough child-speech data available, a child voice can be created without directly adapting an average adult voice, as was the case with [PERSON] et al. 's study [44]. However, it was found that the adaptation of an average adult voice was preferred to the adaptation of the child voice when there were enough adaptation data.