Register Shifts in Whistling: Investigating the Influence of Tongue Shape
Bibliographic record
Abstract
This study examines tongue shape differentiation in whistling and how it influences pitch control during whistling tasks. In previous work, Belyk et al. (2019) used real-time magnetic resonance imaging (rtMRI) to investigate muscles utilized during whistling tasks, however, did not specifically observe register shifts or focus on tongue differentiation. Tongue shape differences in register shifts during opera singing were identified by Bengtson et al. (2023). Additionally, Kaburagi et al. (2018) used MRI to identify a shift in tongue position during whistling in a Japanese participant. This study aimed to examine tongue shape differentiation in whistlers with wide whistling ranges. We hypothesize that participants with broader whistling ranges will use a differentiated tongue shape (Gick et al. 2007) to produce a register shift. Eleven participants were analyzed based on their whistling ranges and were categorized into three groups which included a single octave, 1.5 octaves, and two octaves. Whistling and speech tasks were completed by participants and tongue movements were recorded using ultrasound imaging. Recorded audio was transferred into recording software alongside ultrasound imaging which was analyzed using image processing software. Results of tongue positions from ultrasound imaging will be presented with relevance to whether individuals produce a differentiated tongue shape and/or a shift in their vocal register while whistling, and whether these occurrences were influenced by the participant’s L1. Results illustrated that participants with a broader whistling range displayed greater tongue differentiation, suggesting that there is a connection between whistling range and manipulating the degrees of freedom. Implications will be discussed regarding potential similarities between register changes during whistling and tongue shapes associated with speech, with particular attention to the undifferentiated tongue shape in English /ɹ/ (Delattre and Freeman, 1968).
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".