Students’ Experience in Using Natural Reader Application to Ensure the Accuracy of Their English Pronunciation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since 1960, speech synthesis technology has existed and its applications are now widely available, one of which is the Natural Reader application. Natural Reader is an application that can be used in knowing how to pronounce words in English. This study aims to describe students' experiences with using the Natural Reader application and examine the effect of utilizing the use of the Natural Reader application as a tool to test the accuracy of pronunciation of Mahmud Yunus Batusangkar State Islamic University students, by using the Natural Reader application students can more easily test their English accuracy. The method used by researchers is quantitative method, quantitative research can show data containing numbers that can be obtained by utilizing google form as a means of making a questionnaire. This research shows that the Natural Reader application has the potential to ensure the accuracy of pronunciation in English. So it can be concluded that the Natural Reader device can ensure the accuracy of student pronunciation in English.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it