Effect of years of voice training on chest and head register tongue shape variability
Bibliographic record
Abstract
The transition between head and chest registers in operatic singing has been linked to adjustments in the larynx [Henrich, 2006, LPV 31], vocal tract length [Tokuda et al., 2010, JASA 127], and resonance frequencies [Echternach et al., 2011, JASA 129]. Research on supralaryngeal articulator differences, specifically midsagittal tongue shape differences, during this transition is limited. Our previous case study showed a higher tongue dorsum in head voice for low and mid vowels compared to chest voice [Bengtson et al., 2023, CAA 51]. The current study with ten participants (8 female, 1 non-binary, 1 male, aged 19–23) further explores this and the effect of the years of vocal training. Participants were recorded performing a chromatic scale through their register transition, followed by a whole-tone scale in each register, with both tasks repeated twice on each of seven vowels (/a, e, o, ɚ, i, u, y/). The hypothesis is that vowel-dependent tongue adjustments would be observed, with more experienced singers displaying smaller differences between registers. Tongue shapes were traced using DeepEdge, and target frames were extracted [Chen et al., 2020, ISSP 2020]. Preliminary analysis indicates that the tongue dorsum is lower for chest voice and for participants with more years of voice training.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".