Evaluating automatic speech recognition for L2 pronunciation feedback: a focus on Google Translate
Bibliographic record
Abstract
This study examines the L2 pronunciation feedback provided by the Automatic Speech Recognition (ASR) functionality in Google Translate (GT). We focus on three Quebec Francophone (QF) errors in English: th-substitution, h-deletion, and h-epenthesis. Four hundred and fifty male and female QF recordings of sentences with correctly and incorrectly pronounced final items (e.g. I don’t know who to thank versus tank) were played into GT. Errors were equally divided between mispronunciations leading to real word (thank → tank) and nonword output (thief → tief). As anticipated, we found greater transcription accuracy for correct pronunciations and, among incorrect pronunciations, for real words versus nonwords. Overall, our findings suggest ASR can be highly effective for pronunciation feedback. We also examined transcriptions for gender bias, since ASR systems are often trained on corpora with more male voices, but our concerns proved unfounded: surprisingly, higher transcription accuracy was found for female recordings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".