Exploring automatic speech recognition for corrective and confirmative pronunciation feedback
Bibliographic record
Abstract
Abstract Given that second language pronunciation errors are typically variable, learners would benefit from feedback that both flags errors ( corrective feedback ) and confirms correct pronunciation ( confirmative feedback ). We investigated Google Translate (GT) automatic speech recognition (ASR) transcription accuracy to determine its capacity to provide such feedback, based on Quebec francophone recordings of correctly/incorrectly realized English th-initial, h-initial and vowel-initial items in predictable/unpredictable sentence contexts. Recordings from male and female speakers were used to verify possible gender bias. In predictable contexts, transcription accuracy rates were higher for correct vs incorrect pronunciations; rates in unpredictable contexts for correct or incorrect pronunciations fell midway between the two. GT ASR is thus better at providing confirmative feedback in predictable contexts but corrective feedback in unpredictable contexts. Regardless of context, accuracy was considerably higher on errors leading to real-word than nonword output. Contra the anticipated pattern, female speakers were transcribed with higher accuracy than male.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".