Information Load Predicts Stuttering Events
Clinical Snapshot
PICO Framework
| P — Population | 35 adults who stutter, drawn from the FluencyBank corpus |
| I — Intervention | Words with higher information load (measured by word surprisal using GPT-2 language model) |
| C — Comparator | Words with lower information load/surprisal values |
| O — Outcomes | Likelihood of stuttering events during spontaneous speech production |
Bottom Line
This cross-sectional study of 35 adults who stutter demonstrates that words with higher information load (measured by computational surprisal) are significantly more likely to trigger stuttering events, even after controlling for linguistic factors. The relationship was not moderated by stuttering severity or impact measures. While the computational approach is innovative and the theoretical framework sound, the study's cross-sectional design limits causal inference. The findings suggest that unpredictable words place greater communicative demands on speakers who stutter, potentially informing therapeutic strategies that target high-information contexts. However, clinicians should interpret these results cautiously given limited methodological detail and unknown effect sizes. Further research with prospective designs and detailed stuttering measurement protocols would strengthen the evidence base for clinical application.
Key Findings
P Value: Statistically significant (exact value not provided)
Effect Size: Not reported in abstract
Primary Outcome: Likelihood of stuttering events on words with varying information load
Nnt Or Sensitivity: Not applicable for this association study
Confidence Interval: Not reported in abstract
Clinical Application
High - findings inform therapeutic target selection and treatment planning Relevant to Speech Pathology Australia members treating stuttering; may inform PBS-funded speech pathology interventions under chronic disease management plans Adults who stutter receiving speech pathology services
Abstract
PURPOSE: One potential contributor to the variability of stuttering events is information load, quantified by assessing word predictability based on preceding context. This study investigated whether words with greater information load are more likely to be stuttered using word surprisal as an index while controlling for relevant linguistic variables. We also examined whether this relationship varied with stuttering severity and impact. METHOD: We analyzed spontaneous speech samples from 35 adults who stutter, drawn from the FluencyBank corpus. Word surprisal values were computed using the Generative Pretrained Transformer 2 large language model and analyses controlled for six linguistic predictors of stuttering. We also tested whether clinical measures of stuttering severity and overall impact predicted individuals' sensitivity to surprisal. RESULTS: Higher word surprisal significantly increased the likelihood of stuttering, even after controlling for linguistic covariates. However, we found no evidence that stuttering severity or overall impact moderated this relationship. CONCLUSIONS: These findings suggest that words with greater information load are more likely to elicit stuttering during spontaneous speech. This pattern may reflect increased communicative demands associated with high-surprisal words. The absence of a moderating effect of stuttering severity or impact may indicate that individual differences in coping with communicative pressure, rather than clinical severity alone, shape stuttering variability.
References
- 1.Warner, H. J., Ratner, N. B., & Jackson, E. S. (2026). Information load predicts stuttering events. Journal of Speech, Language, and Hearing Research. https://doi.org/10.1044/2026_JSLHR-25-00833
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