Size of Velopharygeal Opening and Nasality Measurements from Acoustic Features
Bibliographic record
Abstract
Previous research has explored the relationship between nasality measures obtained from acoustic signals [Chen 1997, JASA 102] and direct measurements such as nasal airflow, highlighting promising outcomes. Carignan [2021, JASA 149; 2023, LabPhon 14] reported that nasality measurements from acoustic features (NAF) using machine learning algorithms like PCA regression and XGBoost highly correlate with airflow data. However, it remains unclear how well acoustic measurements align with the actual size of the velopharyngeal opening (VPO). To address this, the current study examines the correspondence between acoustic measurements of nasality and the size of the VPO. We conducted an investigation using running speech samples produced by 4 Canadian English speakers, obtained from the Université Laval X-ray videofluorography database [Munhall et al. 1995, JASA 98]. Using ImageJ software, we tracked the opening and closing movements of the VPO in both nasal and oral segments. Subsequently, we employed the NAF method [Carignan 2023, LabPhon 14] to measure the degree of nasality in the speech samples. We compared the NAF measurements of nasality with the data obtained from the VPO tracking to assess the degree of correlation between the two measures. The results of our study indicate a positive alignment between nasality measurements derived from acoustic signals and the VPO data. This finding suggests that NAF measurements can be a decent predictor of the actual size of the VPO in nasal speech vs. oral speech segments, indicating a positive relationship between NAF, VPO, and nasal airflow. These results have implications for our understanding of nasality production and contribute to the growing body of research on the relationship between acoustic measurements and physiological aspects of speech production. [Work supported by NIH and NSERC.]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".