# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 7 Discussion
In contrast, LSTM is used on the Thai vowel dataset, which is one-syllable words mand not long sentences; therefore, LSTM is not effective for this task. MS is used in conjunction with CNN, and it can distinguish vowel sounds, although the aggregate dataset is more complex due to many dimensions, such as various noises, ages, accents, environments, and physical characteristics (i.e., female vs. male voices). In the same way [46], MS was applied to the speech command recognition (SCR) task and achieved good performance. MS images with a feature size of 125 \(\times\) 80 \(\times\) 1 were used as acoustic features. The light interior search network (LIS-Net) model was applied to the SCR task using the Google Speech Command dataset. The results of the LIS-Net model achieved 97% accuracy. In this research, the vowel speech dataset used is similar to those used in [44; 45] that used a vowel speech dataset for classification with CNN. The classification results were 94% and 99.6% accurate, respectively. This research differs from [44; 45], which focus on Javanese vowel sounds. The dataset in [44; 45] did not describe how to design, collect, and verify data under a linguistic theory. That dataset was recorded from only one speaker. Only 250 Javanese middle vowels sound files were collected, which were divided into 5 classes (/e/, /e/, /o/, and /o/). The dataset in this research work has more diverse dimensions than [44; 45]: the number of speakers, the total audio files, the variety of classes, and diverse sound environments. The recognition model also provides satisfactory results. Vowel pronunciation was applied to classical Arabic phonemes [52], which also differs from this research; in that study, the data consisted of 28 consonants associated with 3 short vowels, and a CNN was used to categorize 84 classes. A total of 6229 recorded items in the dataset were documented online from 85 speakers (81 native and 4 nonnative Arabic speakers). The model obtained an accuracy of 95.77%. Thai vowel recognition using deep learning is compared with vowel recognition tasks using deep learning in Table 7.  
In many works, models are not deployed in real-world situations. As a result, when they are applied to systems, the true impacts cannot be determined. To examine robustness, \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Vowels** & **Methods and Results** \\ \hline Javanese vowels [44; 45] & A CNN model with MFSC is applied to recognize 5 classes of Javanese vowels. The dataset consisted of 250 middle vowels sound files recorded by a Javanese speaker. The results achieved are 94\% and 99.6\% accuracy. \\ \hline Thai vowels [43] & A CNN of the Thai Simple Vowel model with MFCCs is applied. Two datasets are used, female and male. They are collected from 50 informants. Each dataset contains 900 sound files. The output consists of 18 classes. The results show 90.00\% and 88.89\% accuracy in the female and male datasets, respectively. \\ \hline Arabic short vowels [52] & Arabic short vowels using the CNN model are applied to classical Arabic phonemes. The model categorizes 84 classes from 28 consonants associated with 3 short vowels. The online dataset is recorded from 85 speakers producing 6229 records. The model archives an accuracy of 95.77\%. \\ \hline Thai vowels [This work] & A CNN model with MS achieves an accuracy of 98.61\%. The model classifies 18 classes. The dataset consists of 1800 vowel sound files recorded from 50 native speakers. The dataset is designed, collected, and examined by a linguist and a native speaker. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Vowels recognition using deep learning tasks. the CNN model is implemented on the CAPT system in a real situation. The input data received from learners are considered invisible data. When compared with the linguist's and the native speaker's perceptions of the vowel sounds, the CAPT system's vowel detection was 89.81% accurate.