Errors and strategy shifts in speech production indicate multiple levels of representation
Bibliographic record
Abstract
An earlier pilot study [Gick, J. Acoust. Soc. Am. 112, 2416 (2002)] observed that speakers producing challenging sequences of articulatory movements exhibit categorical shifts in production strategies. For example, a speaker may initiate all two-flap sequences with an upward tongue movement, then shift to a downward movement, then shift again. The present study attempts to determine how these strategy shifts can inform phonologists as to levels of representation. Ten subjects produced sequences of flap allophones of English /t/ and /d/ in an experimental paradigm similar to the pilot study. Results show strategy shifts in the speech of 7/10 subjects, such that: (1) shifts are more likely to occur following speech errors; (2) errors are more likely to occur in sequences with more flaps and more conflicts; (3) errors in response to articulatory conflicts occur at multiple levels of representation (segmental, lexical, etc.); (4) strategy shifts occur at multiple levels of representation (subsegmental, segmental, whole word); (5) word frequency does not affect strategy shifts. These results suggest that speech processing is active in parallel at multiple levels associated with phonological representation. [Work supported by NSERC.]
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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 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".