Auditory priming releases Chinese speech from informational masking
Bibliographic record
Abstract
Before an English speech sentence is presented, hearing or reading the sentence without the last key word improves recognition of the last key word if the full-length speech sentence is presented under speech masking but not under noise masking. This phenomenon suggests a content priming effect on releasing speech from informational masking. To determine whether the priming effect extends to tonal Chinese speech, and, in particular, whether it can be induced by the target talkers voice, in the present study, listeners were presented with either same-voice/different-sentence primes or same-voice/same-sentence primes before hearing the target sentence in either two-talker-speech masking or noise masking. Under speech masking, each of the two prime types significantly improved recognition of the last key word in the full-length target sentence, but the content priming is stronger than the voice priming. Under noise masking, same-voice/same-sentence primes had a weak but significant priming effect, but same-voice/different-sentence primes had only a negligible priming effect. These results suggest that both content and voice cues can be used by listeners to release Chinese speech from informational masking, but only content cues are useful for releasing Chinese speech from energetic masking. [Work supported by China NSF and Canadian IHR.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".