Processing pronunciation variation with independently mappable allophones
Bibliographic record
Abstract
• A long-standing merger of /n/ to /l/ in Cantonese has produced [n]- and [l]-initial pronunciation variants that are effectively allophones of syllable-initial /l/ and /n/. • [n] and [l]-initial allophones are distinguishable in perception. • In recognition and encoding paradigms, [n] and [l] allophones are processed neither equivalently nor distinctly when the targets bear the more common [l]-initial allophone. • Error rates are high when the targets bear the [n]-initial allophone. • [n] and [l] are allophonic variants independently mapped to a phoneme, with connection strengths varying. Sound change can present synchronic variation with categorical pronunciation variants. This is the case in Cantonese, where syllable-initial /n/ is merging with /l/, occasionally creating homophones (e.g., lou5 腦 “brain”/ 老“old”) and giving rise to [n]- and [l]-initial pronunciation variants that are allophones. This pronunciation variation offers insight into how variation is processed in spoken word recognition because [n] and [l] in Cantonese are not associated with an orthographic standard. Across four experiments, we examine the perception, recognition, and encoding of Cantonese [n] and [l], and use Bayesian analyses where gradient interpretations are more straightforward. We observe perceptual evidence that these allophones are distinguishable (Exp 2). In recognition (Exp 1) and encoding (Exp 3) paradigms, we find that the [n] and [l] allophones are processed neither equivalently nor distinctly when the targets bear the more common [l]-initial allophone. When the targets bear the [n]-initial allophone (Exp 4), we observe high error rates, and somewhat contradictory results. Altogether, the results suggest that [n] and [l] are allophonic variants independently mapped to a phoneme, with connection strengths varying as a function of the frequency, such that the more common [l]-initial pronunciation demonstrates an overall recognition advantage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".