Effect of speaking rate and contrastive stress on formant dynamics and vowel perception
Bibliographic record
Abstract
Vowel formants play an important role in speech theories and applications; however, the same formant values measured for the steady-state part of a vowel can correspond to different vowel categories. Experimental evidence indicates that dynamic information can also contribute to vowel characterization. Hence, dynamically modeling formant transitions may lead to quantitatively testable predictions in vowel categorization. Because the articulatory strategy used to manage different speaking rates and contrastive stress may depend on speaker and situation, the parameter values of a dynamic formant model may vary with speaking rate and stress. In most experiments speaking rate is rarely controlled, only two or three rates are tested, and most corpora contain just a few repetitions of each item. As a consequence, the dependence of dynamic models on those factors is difficult to gauge. This article presents a study of 2300 [iai] or [i epsilon i] stimuli produced by two speakers at nine or ten speaking rates in a carrier sentence for two contrastive stress patterns. The corpus was perceptually evaluated by naive listeners. Formant frequencies were measured during the steady-state parts of the stimuli, and the formant transitions were dynamically and kinematically modeled. The results indicate that (1) the corpus was characterized by a contextual assimilation instead of a centralization effect; (2) dynamic or kinematic modeling was equivalent as far as the analysis of the model parameters was concerned; (3) the dependence of the model parameter estimates on speaking rate and stress suggests that the formant transitions were sharper for high speaking rate, but no consistent trend was found for contrastive stress; (4) the formant frequencies measured in the steady-state parts of the vowels were sufficient to explain the perceptual results while the dynamic parameters of the models were not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".