Exploring potential age, gender, and first language bias when using Google Voice Typing (GVT) for automatic scoring systems in pronunciation placement tests
Bibliographic record
Abstract
Dictation technology using automatic speech recognition (ASR), such as Google Voice Typing (GVT), has shown promise in scoring pronunciation placement tests, with strong correlations to human rater scores. However, potential biases in these systems must be investigated to ensure fair and accurate assessments. This quantitative study examined gender, first language (L1), and age biases in GVT-based scoring of a pronunciation placement test. Existing recordings of pronunciation placement tests of 1000 university-level English second language students in Canada were examined. The test takers completed a timed task of reading five increasingly complex sentences which were scored by human raters. Regression analyses were conducted with the GVT scores predicting human-rater scores, with age, gender, and L1 input as moderators. Results revealed no significant gender or L1 bias; however, test takers aged 29 and younger were disadvantaged due to an age bias. We conclude that GVT could serve as a reliable tool for scoring pronunciation placement tests if scores are adjusted to mitigate the identified age bias.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".