# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 2 Related Works  
[PERSON] and [PERSON] [16] demonstrated how ASR technology could be utilized in an electronic curriculum of English as a foreign language (EFL) to assist L1 Japanese learners to develop better pronunciation. They used a combination of pre- and post-records as well as survey responses to figure out how learners would react to the ASR program, if they would advance, and which courses would be most beneficial to them. The findings indicated that the learners were mainly happy about the ASR-assisted practice and that they considerably enhanced their intelligibility, particularly those who started with lesser competence. [PERSON] and [PERSON] [17] evaluated how learning methods (visual/verbal), including how the use of ASR software impacts adult learners, improved performance in English as a second language throughout a 12 week curriculum concentrating on pronunciation. According to the findings, the learning strategies produced a large disparity in the pronunciation effectiveness of the reading process across all teams. In the reading process, visual learners outscored the verbal learners. Throughout both the reading activities and live discussions, the mixture of ASR and peer correction resulted in a significant performance. [PERSON] [18] discussed how ASR could be used to train learners to improve their accents in a foreign tongue. First, the elements of effective language instruction were mentioned, as were the limitations of using ASR, and how to cope with them. The author also used the Carnegie Mellon FLUENCY mechanism as an example to demonstrate how such an approach would work. Phonetics and prosody training were highlighted. Eventually, using the FLUENCY process as an instance, the author emphasized the importance of having a platform that adjusts to the user. [PERSON] and [PERSON] [19] studied the comparison in adults' pronunciation effectiveness with the help of an ASR platform with peer assessment as well as individual practice. The respondents were Taiwanese working individuals. During the weekly learning experience, respondents dictated a document to the ASR application, Speechnotes, on its online platform, and then practiced misinterpreted phrases on their own (the comparative group) or with responses from team members (the experimental group). Following the initial intervention, the pronunciation of the trainees was assessed through extensive reading as well as spontaneous discussion activities. [PERSON] and [PERSON] [20] designed a spoken English assistant pronunciation training model based on the Android mobile application. They proposed a lip movement judgement algorithm focusing on ultrasonic identification. This was used to support the conventional voice recognition method in the double feedback judgement, based on an in-depth research and assessment of the verbal English speech correction algorithm and speech feedback process. A dual standard scoring technique was also designed in the smart speech training feedback method to exhaustively assess the verbal trainer's utterance as well as to correct the presenter's speech in real time. The experimental findings demonstrated that the platform's speech precision reached 85 percent, which enhanced the standard of oral English lecturers to some significant degree. [PERSON] [21] explored whether the automated assessment of pronunciation accuracy employing speech recognition advanced technologies could enhance the pronunciation abilities of 105 Thai undergraduate learners learning English in Thailand (88 female, 17 male). A pre-test, post-test architecture was used with intervention and control sampling methods that were reversed over two six week timeframes. Participants in the treatment team were provided entry to an online system where they could record as well as publish their speech for electronic analysis and feedback using SpeechAce, a voice recognition functionality aimed at evaluating pronunciation as well as fluency. [PERSON] et al. [22] experimented with the use of synthesized voice as an asset for formative assessment.