# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
### Contributions in Automatic Thai Vowels Pronunciation Recognition  
In order to accurately recognize pronunciations, there are many tasks that use specific techniques or special tools to analyze them, for example, developing a 3D pronunciation learning system in Chinese [9], using ultrasound with the pronunciation of consonants [10], developing an application that helps to analyze tones in Thai [11], and applying the Praat program for analyzing phonetics to demonstrate the pronunciation of vowels [12; 13; 14; 15; 16; 17]. To increase the accuracy of pronunciation, linguists use the acoustic phonetics method for phonetic analysis. Praat [18] is a popular tool for acoustic phonetics analysis. It is used to display two formant frequencies, which are Formant1 (F1) and Formant2 (F2). In order to display whether the pronunciation of the vowel is correct or incorrect, the location of the tongue must be represented with a specific graph. This is a complex process that requires specialists. The tongue position analysis process by a linguist starts with (1) recording and playing the vowel sound with the Praat program; (2) checking and selecting the range of the vowel sound in the syllable to measure; (3) compiling the average of the F1 and F2 of the vowels that were chosen; (4) in Microsoft Excel, recording the average values of F1 and F2; (5) using Microsoft Excel, Python, or R, creating a vowel graph of native speakers and learners; (6) comparing the graphs of native speakers and learners; and (7) presenting and explaining the incorrect pronunciation from the results to the learners and correcting them. The method for determining pronunciation accuracy with graphs is a complex, time-consuming, and nonreal-time process. The user, moreover, must be an expert or someone with programming knowledge. Therefore, there are few specialists in the field today. So far, there are a few research works conducted to classify Thai vowels pronunciation using an intelligent system. From the literature review, there is no study on automatic CAPT with Thai vowels using AI with a deep learning structure. Therefore, this research aims to design and develop an automatic CAPT system using a deep learning structure for Thai vowel speech recognition. This system is developed for solving the problems of practicing the pronunciation of Thai vowels for (1) nonnative learners or nonstandard Thai speakers and (2) people with pronunciation disabilities; (3) for solving the shortage of specialists in teaching Thai vowels pronunciation; (4) for solving the original process, which is complicated and time-consuming and does not present results in real time; and (5) inventing a new tool for learning languages online that is appropriate for the current situation. A deep learning structure of CAPT for Thai vowels speech is designed to recognize the 18 basic Thai vowels. The pronunciations of the 18 Thai vowels are difficult. Some phonemes have similar characteristics. Therefore, nonnative learners cannot distinguish them and require assistance from an expert. In this research, a convolutional neural network (CNN) is trained over the dataset for Thai vowels speech classification. This model is created to train a computer to recognize vowels like an expert who can identify learners' vowel pronunciation. The existing Thai audio corpus is not suitable for the objectives of this research. Therefore, to obtain theoretically qualitative vowel data in linguistic principles, a new dataset is designed and collected. The dataset used for training this model consists of voices collected from various dimensions in real-life contexts, such as gender, age, accent, environment, and noise. The major contributions to this work are as follows:  
1. This research proposes a noisy dataset that is collected from standard Thai speakers from various dimensions. The dataset is designed, collected, and examined by a linguist. 2.