# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 5 Results  
This experiment utilizes combinations of two acoustic feature inputs and three model settings as follows: (1) the MFCC acoustic features combined with a baseline CNN model, (2) the MS acoustic features combined with the baseline CNN model, (3) the MFCC acoustic features combined with the baseline LSTM model, (4) the MS acoustic features combined with the baseline LSTM model, (5) the MFCC acoustic features combined with the optimized model, and (6) the MS acoustic features combined with the optimized model. Table 3 presents the different experimental results. The parameters are set with 32 batch sizes and 500 epochs. The MS acoustic features combined with the baseline CNN model achieves the lowest accuracy at 88.89%. In the third and fourth experiments, the MFCC and MS acoustic features on the baseline LSTM model achieve low accuracy, 94.44% and 90.00%, respectively. LSTM layers are beneficial for learning long-term contextual dependencies from long sequences. Here, in contrast, LSTM is used on the Thai vowel dataset which is one-syllable words, not long sentences. Therefore, LSTM is not outstanding in this task. The optimized CNN model combined with MS acoustic features achieved improved accuracy of 98.61%. The result of the optimized CNN model shows that the MS acoustic features perform the best for Thai vowel classification. The line graphs of the accuracy and loss of the optimized CNN models combined with MFCC or MS acoustic features are presented in Figure 6a,b. The visualization shows the line graph that compares the accuracy and loss of the training and testing models from 0 to 500 epochs. The optimized MS acoustic features combined with the optimized CNN model outperform the optimized CNN combined with MFCC and achieve the best \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Experiment Settings** & **Accuracy (\%)** & **Error of the Model (Loss)** \\ \hline
1. & MFCC + baseline CNN model & 93.33 & 0.60 \\ \hline
2. & MS + baseline CNN model & 88.89 & 0.65 \\ \hline
3. & MFCC + baseline LSTM model & 94.44 & 0.25 \\ \hline
4. & MS + baseline LSTM model & 90.00 & 0.45 \\ \hline
5. & MFCC + optimized CNN model & 98.06 & 0.12 \\ \hline
6. & MS + optimized CNN model & 98.61 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results from the different experiments (k-fold = 5). accuracy of 98.61% as shown in Figure 5(b). The loss values are presented in Table 3. The baseline CNN model with the MFCC or MS acoustic features obtains higher loss values and has more overfitting problems than the other models. The optimized CNN model combined with the MFCC or MS acoustic features can reduce the overfitting problems as illustrated in Figure 5(a,b). For the error analysis, the confusion matrix of the optimized CNN model for Thai vowel recognition is shown in Figure 7. For the details of misclassification, 10 of the 18 classes have a 0% error rate. The 'user' /r/ vowel is the class that has three mispredictions. In the confusion matrix, the most perplexing Thai vowel pairs are (''m=' /r/) and ('' /m/). These sounds are similar, which can be explained by linguistic theory as they share the  
Figure 6: The accuracy and loss of the optimized CNN model. (**a**) MFCC + the optimized CNN model. (**b**) MS + the optimized CNN model. same characteristics. The (''_''''/) and (''/)/ pronuncinations both use the back part of the tongue [61]. As a result, the Thai vowel recognition model may be confused. Table 4 shows the precision, recall, and F1 scores of the CNN model for classifying each Thai vowel. The lowest F1 score on the dataset is 0.89 for (''_''/). The F1 score results are relevant to the confusion matrix. On the other hand, the highest F1 score (i.e., 1.00) on the dataset can be seen for (''_''/), (''_''/), (''_''/:), (''_''/:), and (''_''/:). Based on error analysis and the evaluation of the CNN, an accuracy of more than 95% was achieved. This optimized CNN model was implemented on the automatic computer-assisted pronunciation training (CAPT).