Exploring the speech chain experimentally: English sound identification accuracy is modulated by phonological factors across adult speakers and listeners
Bibliographic record
Abstract
Speech perception is influenced by communicator characteristics and linguistic factors. Using a multi-speaker sound identification task, this study explored the relative contributions of speaker and listener variability. 384 triphones were extracted from 21 native English speakers reading a list of phonetically rich words. The words and the resulting triphones were balanced in terms of sound frequency (five groups), context prominence (three tiers), and word stress (levels: monosyllabic, unstressed/stressed polysyllabic). The mean length of the words was 6.0 sounds (range: 3–14). A total of 22 listeners were instructed to type the consonants they identified in audio clips drawn randomly from all the speakers using a Latin square design yielding a total of 16 408 responses. The mixed effects logistic regression model showed identification accuracy was lowered by lower frequency sounds (z = 4.0), less prominent context (z = 5.7), lack of stress (z = 3.7), and number of sounds in the word from which the triphone was extracted (z = 3.3). Speaker intercept standard deviation was 87% greater than that of listeners. The results corroborate a model of sound identification that considers frequency and context prominence. The results suggest that our experimental design is sufficiently powered to further explore the components of the speech chain.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".