Effective Elicitation of Stuttering in Magnetic Resonance Imaging Data Collection Using a Suite of Connected Speech Tasks
Bibliographic record
Abstract
PURPOSE: Articulatory behaviors during moments of stuttering have been understudied, largely due to the technical difficulty of collecting such data. Tracking moving articulators during stuttering requires advanced instrumentation, and eliciting stuttering in a lab setting poses challenges for experimental design. To address these difficulties, we present a novel methodology that combines real-time vocal tract magnetic resonance imaging (MRI) with a suite of connected speech tasks to elicit stuttering. METHOD: A high-performance 0.55 T MRI system, with a custom eight-channel upper airway coil and a spiral balanced steady-state free precession pulse sequence, was used to acquire real-time MRI speech production data from seven adults who stutter. During scans, participants performed three connected speech tasks that incorporate stuttering-inducing factors: (a) passage reading, (b) short interviews with the experimenter, and (c) picture description within a time limit. Speech tasks were interleaved with one another. RESULTS: Each participant produced over 100 stuttered words, covering various disfluency types and linguistic features. Fluent and disfluent productions of the same words were elicited, enabling direct articulatory comparisons. Participants did not show a significant decrease in the percentage of syllables stuttered (%SS) inside the scanner compared to outside, suggesting that our protocol effectively mitigated fluency-enhancing factors during scanning. %SS in each speech task varied substantially across participants, justifying the inclusion of multiple task types. Interleaving different tasks helped maintain a stable %SS throughout. The collected real-time MRI vocal tract videos reveal meaningful articulatory behaviors during stuttering that are not detectable via acoustics alone. CONCLUSIONS: The suite of specially designed speech tasks was effective in eliciting stuttering during real-time MRI data collection. Combining these speech tasks with dynamic MRI technology offers a powerful approach to studying the articulatory mechanisms of stuttering. In addition to real-time MRI, these speech tasks have the potential to be combined with other experimental instrumentation to facilitate collecting data specifically during stuttered speech.
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".