The Effects of Duration on Human Processing of Reduced Speech
Bibliographic record
Abstract
Word recognition of spontaneous speech is influenced by many cues in the signal. The present study investigates the effects of duration on human processing of spontaneous speech. Participants were presented with visual and audio representations of sentences that had one to four words taken out and replaced with a silent gap. Silent gaps were either kept the same as the original duration or manipulated to be 0.5 times shorter or 2 times longer than the original length of the words that had been replaced. Participants were asked to respond using a keyboard to type which word or words they believed fit in the gap. Subsequent responses were classified as either correct or incorrect. The results demonstrate that having access to any contextual auditory information increases correct response rates. This indicates that there are likely cues in the auditory context that allow the listener to resolve the target items that are not present when the trials are presented visually. We also find a trend in the data which seems to indicate that shortened gap targets are harder to predict than the other two conditions (original length and lengthened gap), and that the lengthened gap targets are consistently easier to predict. We interpret this result as indicating that when listeners have additional processing time it allows them to more accurately identify the missing words. Further, we believe that there are durational constraints in the mental lexicon during word selection processes which suggest a mechanism of activation and competition that has been used in various models of spoken word recognition.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".