# A Preliminary Study of Model-Generated Speech
## 4 Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Subcategories/Description** & **Tendency** \\ \hline \multirow{3}{*}{Syllable structure} & \multirow{3}{*}{CVE \(>\) CV \(>\) CSV \(>\) CSV \(>\) CSV \(>\) CSV \(>\)} & VS (64%) \(>\) VN (61%) \(>\) VV(58%) \(>\) \\  & & V (55%) \(>\) V (25%) \(>\) VSV (21%) \(>\) \\  & & syllableid consonants (5\%) \\ \hline \multirow{3}{*}{Vowel/ Nucleus} & \multirow{3}{*}{V \(>\) V�̄ \(>\) Rront V \(>\) back V} & i[i] (65%) \(>\) e[e](64%) \(>\) a[a] (61%) \(>\) \\  & & u[u](57%) \(>\) e[u] (55%) \(>\) o [o] (49%) \\ \cline{1-1}  & & en[\(\mathrm{\SIUnitSymbolMicro]}\) (54%) \(>\) in[i] (20%) \(>\) an[i] (17%) \(>\) \\ \cline{1-1}  & & un[\(\mathrm{\SIUnitSymbolMicro]}\) (15%) \(>\) syllable consonants (8%) \(>\) \\ \cline{1-1}  & & on[\(\mathrm{\SIUnitSymbolMicro]}\)(0%) \\ \hline \multicolumn{2}{c}{Consonants} & & \\ \hline \multirow{3}{*}{Onset} & \multirow{3}{*}{
\begin{tabular}{c} manner of articulation \\ (MOA) \\ \end{tabular} } & lateral (61%) \(>\) fricatives (59%), \\  & & affricates(59%), nasals (59%) \(>\) zero \\  & & onset (57%) \(>\) stops (56%) \\ \hline \multirow{3}{*}{Onset} & \multirow{3}{*}{place of articulation (POA)} & glottal (60%) \(>\) velar (59%) \(>\) bi-labial \\  & & (58%) \(>\) zero onset (58%) \(>\) coronal (57%) \\ \hline \multirow{3}{*}{Onset} & \multirow{3}{*}{voiced feature} & Voiced (60%) \(>\) voiceless (57%) \\ \cline{1-1}  & & aspirated feature & unaspirated (58%) \(>\) aspirated (54%) \\ \hline \multirow{3}{*}{Tones} & & \\ \hline \multirow{3}{*}{Tone} & \multirow{3}{*}{total contour 1} & level tone (T1, T5, T7) (57%) \(>\) contour \\  & & tone (T2, T3, T6) (55%) \\ \cline{1-1}  & & & H initial T value (T2, T5, T8) (65%) \(>\) L \\ \cline{1-1}  & & initial T value (T3, T4, T7) (49%) \\ \hline \multirow{3}{*}{Tone} & \multirow{3}{*}{duration} & checked tone (T4, T8) (64%) \(>\) unchecked \\ \cline{1-1}  & & tone (T1, T2,T3,T5,T6,T7) (57%) \\ \hline \hline \end{tabular}  
* Checked tones are not included. 2 T1 /33/ and T6 /35/ are not included.  
\end{table} 
Table 2: Accuracy of the three models compared to the 2015 Chaoshan dictionary. rates varied from 60% to 57%. One should note that coronal unmarked features are considered to be acquired first or with the least restriction on their combination with the lowest rate of accuracy. Voiced consonants were easier to predict correctly compared to voiceless ones; in contrast, the aspirated consonants were harder to predict correctly. The difference between the voiced/voiceless and aspirated/unaspirated consonants was the smallest, with a value of 3-4%. Finally, tonal accuracy was analyzed in terms of tone contour, register, and duration. The accuracy of the prediction of level tones was found to be higher than for contour tones, indicating no significant difference. However, the predicted value for the H initial tone, at 3 on the five-point Chao scale, was higher than that of the L initial tonal value (65% vs. 49%). The checked tone accuracy (64%) was higher than the unchecked tone accuracy (57%); the former is always acquired later by children. To sum up, the predicted sounds made by the machine learning models displayed very different phenomena compared to human language learning. The differences between the model-generated sounds and the human-acquired sounds were, thus, compared. In machine learning tasks, training a complicated neural network model such as seq2 seq requires a substantial amount of data to establish an effective model. However, due to the limited number of entries in the two Chaoshan dictionaries currently available, only 5523 data items were available for training and testing. Although this quantity may seem somewhat insufficient for training a seq2 seq model, it is quite adequate for traditional machine learning models such as DireCTL+ and Sequitur, especially for machine transliteration tasks. The experimental results indicate that the performance of seq2 seq models still surpasses that of DireCTL+ and Sequitur.