## 2 Related Works
\begin{table}
\begin{tabular}{c c} \hline
**Title** & **Contribution** \\ \hline \multirow{6}{*}{Research on Open Oral English Scoring System Based on Neural Network [9]} & A scoring system for open-spoken English using Neural Network (NN) based on the user’s recorded voice at phonetic and text levels. The research separately scores the spoken content and spoken speech, in which the spoken content results from an external speech recognition engine and the spoken speech is scored using an in-house NN model. \\ \cline{2-3}  & A Convolutional Neural Network-Based Intelligent Evaluation Algorithm for the Quality of Spoken English Pronunciation [10] & A Convolutional Neural Network (CNN) is used to evaluate the quality of spoken English pronunciation. The research directly feeds the voice signal into the CNN model to extract each feature rather than using any speech recognition engine to transcribe the voice into text. \\ \hline \multirow{3}{*}{Improving English Pronunciation: An Automated Instructional Approach [11]} & An experiment that checks which group of children is willing to improve their English pronunciation using several English films and English-language learning software such as Speech-To-Text software engine \\ \cline{2-3}  & The foreigners are told to speak specific Korean sentences and tested in two scenarios: with and without text. The spoken sentences are then fed into a Bidirectional Long Short-Term Memory (BLSTM)-based model that measures five holistic proficiency scores: segmental accuracy, phono-logical accuracy, fluency, pitch, and accent. \\ \hline \multirow{3}{*}{Review of hardware implementation of Speech-To-Text Engine for Jawi Character [13]} & A hardware implementation for helping students to read Jawi characters more effectively using Speech-To-Text tools \\ \cline{2-3}  & This research shows the overview of multiple features of the SpeechRater\({}^{5M}\) v. 5.0 Engine [14] & SpeechRater engine to evaluate the speech performance of foreigners speaking English. \\ \hline \multirow{3}{*}{Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq [15]} & This research developed a FAIRSEQ extension for Speech-To-Text modelling tasks such as STT translation and speech recognition called FAIRSEQ S2T. \\ \cline{1-1} \cline{2-3}  & This research developed a FAIRSEQ extension for Speech-To-Text modelling tasks such as STT translation and speech recognition called FAIRSEQ S2T. \\ \hline \end{tabular}
\end{table} 
Table 1: Previous Related Works Contribution as a Comparison. Otherwise, it will give a non-optimal result. The challenge from [9; 10] is that the model was developed from scratch with NN or CNN. It was specifically designed for English, thus, cannot be used for Korean effectively and efficiently, unlike the open-source STT engines available for multiple languages. In [14], although the SpeechRater is used to conduct automatic scoring, it is not considered an STT engine and is mainly used to generate a TOEFL score. Thus, it is also not suitable for Korean. As for [12], although the BLSTM model is designed for Korean and can measure multiple holistic proficiency scores, the research only developed the model as is, without it being implemented to a particular platform or application to be used for everyday users. Even though the research results in [15] show that the FAIRSEQ S2T extension improves model performance, it is tested for English only. Thus it might show a different result if used in Korean. Apart from what has been mentioned in the table, there are other exciting research results, such as [16; 17; 18; 19]. In [16], a method was developed to enrich speech recognition text results by processing the sentence boundaries and speech disfluencies that are proven suitable for English. In [17], a method was shown that measures the readability of STT transcribed text based on the correctness of responses to comprehension questions, speed of reading and responding to questions, and a subjective rating of passage difficulty.