Perception and recognition of English /s/ and /ʃ/ with varying acoustic-auditory contrast
Bibliographic record
Abstract
Seminal work (Newman et al., JASA, 2001) found that listeners’ responses to talkers with more variable fricative productions were slower, though listeners’ ability to categorize the varied fricatives was robust. The current study selects North American English-speaking talkers with /s/ and / ʃ/ productions that varying in the magnitude of the acoustic-auditory contrast. With selected speech samples, listeners were asked to categorize (1) the isolated fricative (C- only), (2) the fricative-vowel sequence (CV), or (3) to complete a speeded-shadowing task where listeners were auditorily presented with the full words and asked to identify the words by repeating them as quickly and accurately as possible. Data were analyzed with Bayesian methods. The fricatives from talkers with greater contrast were identified more accurately and more quickly, with a greater effect size for the C-only condition and /s/ productions. This suggests listeners leverage information from the formant transitions to differentiate these fricatives. The speeded-shadowing results indicate the participants are faster at identifying the words with less acoustic-auditory contrast. This is the opposite of the expected pattern. Coupling C-only, CV, and word-level responses paints a more accurate picture of how talker differences in auditory- acoustic contrast affect categorization and intelligibility.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".