# A Preliminary Study of Model-Generated Speech
## 5 Discussion
### The Sounds Generated by the Models Are Governed by the Sequence
The argument that the more following vowel candidates there are, the smaller the chance the three models can predict the consonant when divided with respect to their voiced feature still holds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subcategory** & **Model\_acc** & **Rank** & **Distribution** & **Rank** \\ \hline Labial & 58\% & 3 & 13\% & 3 \\ Coronal & 56\% & 5 & 47\% & 1 \\ Velar & 60\% & 1 & 19\% & 2 \\ Glottal & 60\% & 1 & 10\% & 4 \\ Zero onset & 57\% & 4 & 10\% & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the model-generated speech and consonants with respect to the POA and their following vowel distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subcategory** & **Model\_acc** & **Rank** & **Distribution** & **Rank** \\ \hline Stops & 56\% & 6 & 39\% & 1 \\ Fricatives & 59\% & 2 & 20\% & 2 \\ Affricates & 59\% & 2 & 16\% & 3 \\ Nasals & 59\% & 2 & 8\% & 5 \\ Lateral & 61\% & 1 & 7\% & 6 \\ Zero onset & 57\% & 5 & 10\% & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the model-generated speech and consonants with respect to the MOA and their following vowel distribution. Table 6 shows the accuracy of the model-generated speech and the distribution of vowels following different consonants with respect to the aspired feature. For example, the distribution of vowels occurring after an unaspirated consonant was about 30% (5523 items), ranking first. In contrast to the MOA, POA, and voiced feature, the greater the chance of different vowels following an unaspirated consonant, the higher the prediction value made by the three models, with 58%, also ranking first. This means the same pattern was found both for the model accuracy and the following vowel distributions. To sum up, a similar pattern was revealed both in the prediction made by the three models and the distribution of the following vowels, showing that the behaviors of the major features--the MOA, POA, and voicing--differ from those of the minor and aspirated features. One may speculate that the onset prediction of the three models and the distribution of the following vowels may be related to the sonority sequence. If we postulate that the sonority hierarchy is essentially uniform across languages, once a word has a legal syllable structure in one particular language, meaning that its minimal sonority distance in this language is met, it partially fulfills the phonotactic constraint. If so, the reason why some syllable combinations are less common is probably due to a preference for making the sonority distance as large as possible. Thus, when selecting the onset in Chaoshan, for example, a stop is preferred to a nasal and then a liquid because the following component is either a glide or a vowel, which are always ranked as the most sonorant. This regulation of phonotactic constraint in Chaoshan determines the word combination. Concerning feature geometry, if the aspirated consonant is described as [+/ - spread glottis] under the supervision of the laryngeal and [+/ - consonant; +/ - sonorant], we may wonder about how the behaviors of consonants with aspirated features fit with the MOA, POA, and voicing. This brings us to the next question: Why are aspirated consonants synchronized with the distribution of the following? However, only MOA, POA, and voiced features manifest a similar pattern. One might postulate that POA, MOA, and voicing are major features with which to describe consonants, whereas aspirated features are not. Of course, more research is needed to investigate this issue more fully. Table 7 shows the accuracy of the model-generated speech and the distribution of the consonants preceding different types of vowels. We may take VN as an example. The consonants preceding the VN type constitute about 34% (5523 items), ranking first, whereas the accuracy of VN is 59%, ranking second.