Mobile speech recognition software: A tool for teaching second language pronunciation
Bibliographic record
Abstract
This study examines the impact of the pedagogical use of mobile automatic speech recognition software (ASR) on the acquisition of the French vowel /y/ in production and perception. The participants were 42 beginner French students with no previous training in French phonetics and exposure to speech recognition software. They were divided into three experimental groups: (1) the ASR Group used an ASR application installed on their mobile devices to complete weekly pronunciation activities, with immediate written visual (textual) feedback provided by the software; (2) the Non-ASR Group completed the same weekly pronunciation activities in individual weekly sessions with a teacher, who provided immediate oral feedback using recast and repetitions; finally, (3) the Control Group participated in weekly individual meetings “to practice their conversation skills” with a teacher, who provided no pronunciation feedback. Following a pre-test/post-test design, our findings indicate that the ASR Group outperformed the other groups in French /y/ production, but not in perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".