Speaking to a metronome reduces kinematic variability in typical speakers and people who stutter
Bibliographic record
Abstract
BACKGROUND: Several studies indicate that people who stutter show greater variability in speech movements than people who do not stutter, even when the speech produced is perceptibly fluent. Speaking to the beat of a metronome reliably increases fluency in people who stutter, regardless of the severity of stuttering. OBJECTIVES: Here, we aimed to test whether metronome-timed speech reduces articulatory variability. METHOD: We analysed vocal tract MRI data from 24 people who stutter and 16 controls. Participants repeated sentences with and without a metronome. Midsagittal images of the vocal tract from lips to larynx were reconstructed at 33.3 frames per second. Any utterances containing dysfluencies or non-speech movements (e.g. swallowing) were excluded. For each participant, we measured the variability of movements (coefficient of variation) from the alveolar, palatal and velar regions of the vocal tract. RESULTS: People who stutter had more variability than control speakers when speaking without a metronome, which was then reduced to the same level as controls when speaking with the metronome. The velar region contained more variability than the alveolar and palatal regions, which were similar. CONCLUSIONS: These results demonstrate that kinematic variability during perceptibly fluent speech is increased in people who stutter compared with controls when repeating naturalistic sentences without any alteration or disruption to the speech. This extends our previous findings of greater variability in the movements of people who stutter when producing perceptibly fluent nonwords compared with controls. These results also show, that in addition to increasing fluency in people who stutter, metronome-timed speech also reduces articulatory variability to the same level as that seen in control speakers.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".