Sorry, Not Sorry: The independent role of multiple phonetic cues in signaling the difference between two word meanings
Bibliographic record
Abstract
We examine the use of multiple subphonemic differences distinguishing homophones in production and perception, through a case study focusing on the distinction between two polysemous senses of the English word "sorry" (apology vs. attention-seeking). An analysis of production data from voice actors revealed significant and substantial durational differences between the two meanings. Tokens expressing an apology were longer than attention-seeking tokens, and the situational intensity of the context also independently affected duration. When asked to identify the meaning in a two-way forced-choice task after hearing each token spliced out of its context, listeners were above chance (64.7% accuracy) in identifying the intended meaning, and their responses were significantly correlated with the duration, intensity, and intonation contour (but not mean F0) of the productions. In a second perception task, listeners heard tokens of "sorry" that had been systematically manipulated to vary in duration, intensity, and intonation contour, with responses indicating that each of these dimensions played an independent role in listeners' judgments. The results highlight the importance of broadening the scope of research on the use of subphonemic detail during lexical access and considering a wider range of lexical and non-lexical factors that condition variability on multiple acoustic dimensions, in order to work toward a more accurate picture of the systematic variability available in the input and tracked by listeners.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".