Accessing psycho-acoustic perception and language-specific perception with speech sounds
Bibliographic record
Abstract
Abstract In this paper we review the results of four speech perception experiments that explore the difference between language-specific perception and psycho-acoustic auditory perception. The first two experiments examine this difference with voiceless fricatives by American English and Dutch listeners. The second series of experiments explores the perception of consonant palatalization by American English and Russian listeners. These experiments examine this processing difference through two tasks: speeded AX discrimination (“same” or “different” response) and similarity rating. The fast-paced nature of the AX discrimination task is designed to bypass linguistic processing and hone in on pure auditory similarity. The similarity rating task asks listeners to compare two stimuli at a more leisurely pace and language-specific perception is evaluated. The results of these experiments suggest that psycho-acoustic perception can be evaluated apart from linguistic perception. Other work using this experimental paradigm, however, has found language effects in both AX discrimination and rating tasks (Boomershine et al., The impact of allophony vs. contrast on speech perception, Mouton de Gruyter, 2008; McGuire, Phonetic category learning, The Ohio State University, 2007). We reconcile our findings with the contrary results by demonstrating that language effects tend to appear in the longer response latencies that naturally allow for linguistic processing and are attenuated in the fast responses.
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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 | 0.003 |
| 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) | 0.002 | 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".