## 6 Conclusions and Future Studies  
This paper developed a mobile application that automatically generates a score of how well foreigners speak Korean sentences using STT technology and the Sentence Transformer framework for Python to check the similarity to help them increase their Korean pronunciation. The application shows a pre-defined Korean sentence on the screen for the user to read and record themselves using the voice recording function inside the application. Once the user records their voice, they can upload the audio file to the server that will transcribe it into text using the STT engine and then check the similarity between the original pre-defined Korean sentence and the transcribed text. The similarity score result is in percent value. Thus the generated score is based on a combination of the user's ability to pronounce the sentence correctly and the good performance of the STT engine by being able to transcribe the text regardless of the user's accent. The STT engine's performance is essential because this paper not only implements the auto-scoring feature but also gathers the user's voice, score, and transcribed text from various open-source STT engines as the initial dataset for the ideal system. The application can communicate with the server using a Rest API developed in this paper. The Rest API has two components to handle the system's different functions. The PHP Rest API handles the audio file, i.e., uploading and managing the audio file. In contrast, the Python Rest API is responsible for converting the raw audio file into the correct extension and specification, performing the STT procedure, and executing the similarity check function. The results from the Python Rest API are returned to the PHP Rest API to be formatted in JSON before sending the result to the mobile application. The Rest API receives the audio file, pre-defined Korea sentence, and selected STT option as the parameters. After processing, the Rest API will return the transcribed text from the selected STT engine along with the similarity score, which is then displayed in the mobile application. The performance evaluation is conducted to measure the response time and consistency. The response time measures how long the user taps the 'upload' button until the result is displayed on the screen, measured in 3.7 s as the overall average. The consistency measures how consistent the transcribed text and similarity score results are. After testing, it is shown that Google STT, Microsoft, and IBM show a consistent figure showing the exact transcribed text and score. In contrast, Naver Clova is the least consistent, showing eight different transcribed texts and scores. For future studies, this application will be distributed to a group of foreigners who can speak Korean adequately as a volunteer to collect all information (audio file, transcribed text, and similarity score). Then, the information will be compiled to create a dataset for developing an ML or DL-based model that can automatically generate the score through a more profound analysis, semantically and holistically, instead of being based only on similarity. Furthermore, a digital game-based learning approach that combines learning objectives, methods, and outcomes into an interactive game that offers multiple advantages compared to traditional teaching methods and an automatic lexicon analyzer can be added to the mobile application. With this approach, the mobile application will provide a game with multiple levels for foreigners to train their proficiency in speaking Korean. The game's level will start by ordering foreigners to speak simple Korean daily words and finish with complex sentences along with specific tests such as filling in the blanks or speaking the correct answer based on the question. In addition, to increase the depth of the application, a Content Management System will also be added to provide an administrator to efficiently manage the content or asset of the game, including words/sentences used in every level, the scoring system, the penalty system, etc.