Apparent vocal tract length versus /s/ acoustics in a large multilingual corpus
Bibliographic record
Abstract
The acoustics of /s/ are often studied for their socioindexical importance, especially as a cue to gender identity. It can, however, be difficult to distinguish socially meaningful variation from that which is introduced by anatomical differences between speakers—notably, by differences in vocal tract length (VTL). This differs from the situation for vowels, where formant frequencies scale predictably with VTL. A similar relationship might hold in sibilants, if front cavity length—and thus main spectral peak frequency—is roughly proportional to VTL. Alternatively, speakers may compensate for differences in VTL by subtly adjusting their articulation to achieve a particular acoustic target. To explore these possibilities, I examine the relationship between speakers’ apparent VTL (estimated from vowel formant measures) and the peak frequency of their /s/ productions in a multilingual acoustic dataset constructed from large read speech corpora. Preliminary results from nearly 1000 speakers across eight languages reveal that there is generally an inverse relationship between VTL and peak. Although its strength varies across languages, it appears stronger in Afrikaans, Czech, and English, more modest in French, Japanese, and Korean, and weaker or near zero in Mandarin and Arabic. This suggests VTL may influence the interpretation of patterns of interspeaker variation in sibilant acoustics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".