Frequency and context prominence effects on English allophone perception: Identification of /r/ and /<b> <i>θ</i> </b>/
Bibliographic record
Abstract
Purpose: External evidence from multiple cross-linguistics studies suggests the importance of acoustic clarity of speech sounds in words can vary for decoding speech (Gurevich & Kim, 2023b). Dependence on acoustic salience is modulated by the structure and function of words (Bell et al., 2003; Jun, 2011; Jurafsky et al., 2001; Scharenborg et al., 2016). This perception study of English examines the functional importance of positional allophones to intelligibility. Methods: Speech produced by 21 speakers reading five randomized lists of 308 words with high coverage of English allophones (from Gurevich & Kim, 2023a) was presented to 11 naïve listeners for identification using a Latin-square design. The identification accuracy of the highest and the lowest frequency sounds were compared in their most and least prominent positions. Results: A total of 257 perceptual judgements were analyzed. Word-initial prevocalic /r/ and /θ/ had 97% and 75% accuracies, respectively; the pre-consonantal /r/ and inter-consonantal /θ/ had 95% and 43% accuracies, respectively. Conclusions: The results experimentally corroborate the expected hierarchy where higher frequency allophones in prominent contexts show higher accuracy compared to lower frequency allophones in less prominent contexts. Investigating allophones in additional languages will explore the effect of phonology on perception.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".