Intelligibility, recall, and voice evaluation across accents
Bibliographic record
Abstract
Speech elicits variable responses from listeners. Voices can vary in their intelligibility, how well listeners can recall messages produced by the voice, and what kind of social evaluation it explicitly or implicitly evokes. These different responses may be due to individual-specific attributes within a voice or the accent it carries. For example, familiar or more standard language varieties may be more intelligible producing more easily recalled, and eliciting more positive social evaluations from listeners. The current study uses 35 English-speaking voices from 7 different language backgrounds that vary in familiarity and prestige to the listener (n = 430) population, which is a representative heterogeneous sampling from the local university community. Specific voices were chosen from a larger data set based on their acoustic similarity. Listeners either completed a speech transcription task (quantifying intelligibility) or a cloze task (quantifying recall) and all listeners provided an evaluation of the voices’ likability and perceived comprehensibility. Bayesian data analysis is used to quantify and characterize the relationship between a voice’s intelligibility and how well it is recalled, and whether this relationship is predicted by social evaluation and listener experience. These results have implications for theories of speech recognition and how listeners process accents.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".