# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 7 Discussion  
This research uses AI with a deep learning model to automate computer-assisted pronunciation training (CAPT) with Thai vowels. The recognition model for the standard 18 Thai vowels is an essential function for automatic speech recognition. The model is applied to recognize learners pronouncing vowel sounds. To be effective in recognition, proper data and structure are required for training the model. The existing Thai audio corpus cannot be used for the objectives of this research. Therefore, to obtain theoretically qualitative vowel data in linguistic principles, a new dataset was designed, collected, and verified based on linguistic theory by a linguist. The dataset was gathered from native speakers in real-world scenarios, so the CAPT system may be used everywhere and at any time. The dataset consisted of noise at 30-40 dB SNR such as vehicles from the road, people talking, wind at the park, and animal sounds. Therefore, the available data are categorized as the \"noisy Thai vowels\" dataset. The sounds were recorded with a mobile phone by 50 Thai native speakers. The total number of vowels were 1800 sound files divided into 18 classes. The CNN model in this research can reduce the frequency variation of the acoustic features and extract appropriate features. This research preserves the sizes of the feature maps with padding strategies as in [39]. The CNN model reduces the spectral variability in the input features with max pooling as done in [38] and decreases the overfitting problem with the dropout strategies as in [58]. The ELU adopted in the CNN model provides outstanding results. It corresponds to those reported by [51; 59]. In [50], ELU was applied to each LFLB. CNN-LSTM networks were constructed to learn local and global emotion-related features from speech and log-Mel spectrograms. The 2D CNN_LSTM network that used the ELU activation function achieved recognition accuracies of 95.33% and 95.89% in Berlin EmoDB in speaker-dependent and speaker-independent experiments. The ELU facilitates faster CNN learning and led to higher classification accuracies. The benefits of those strategies improve the performance of the Thai vowels CNN model. Given the acoustic features of the model, the time and frequency extension techniques in [39; 43] can be used for this CNN model. The results indicate that the optimum MS acoustic features are 11 \(\times\) 128 (time \(\times\) frequencies). The results of this study differ from those in [43], which applied the CNN model with MFCC acoustic features. Two datasets (female and male) were used in that work. The suitable acoustic feature sizes for the two datasets were different. They were 11 \(\times\) 40 and 11 \(\times\) 64, respectively. The most accurate rates of the CNN model were 90.00% and 88.89% for female and male voices, respectively. Another difference is that in this study, the dataset is a combination of female and male voices. This study also differs from [49], which used MFCC acoustic features with CNN and Laplace HSMM for neonatal bowel sounds. The sequence of 24 MFCCs long was applied to the input to feed into the 1D CNN model. The two classes were peristalsis and no peristalsis. The four convolutional layers were contained in that work. The resultshad accuracies of 89.81% and 83.96% AUC, respectively. In those works, MFCC used a logarithmic frequency scale and DCT, while in this study, MS uses a linear frequency scale. Therefore, different types of sounds and acoustic features lead to different sizes of acoustic feature settings in terms of frequencies and model structure. According to the results, the optimized CNN model with Mel spectrogram provides outstanding performance with 98.61% accuracy, which is higher than MFCC with the baseline LSTM model and MS with the baseline LSTM model, which had accuracies of 94.44% and 90.00%, respectively. LSTM layers are beneficial for learning long-term contextual dependencies from long sequences.