Spectral Coefficient Analyses of Word-Initial Stop Consonant Productions Suggest Similar Anticipatory Coarticulation for Stuttering and Nonstuttering Adults
Bibliographic record
Abstract
A longstanding hypothesis about the sensorimotor mechanisms underlying stuttering suggests that stuttered speech dysfluencies result from a lack of coarticulation. Formant-based measures of either the stuttered or fluent speech of children and adults who stutter have generally failed to obtain compelling evidence in support of the hypothesis that these individuals differ in the timing or degree of coarticulation. Here, we used a sensitive acoustic technique–spectral coefficient analyses–that allowed us to compare stuttering and nonstuttering speakers with regard to vowel-dependent anticipatory influences as early as the onset burst of a preceding voiceless stop consonant. Eight adults who stutter and eight matched adults who do not stutter produced C 1 VC 2 words, and the first four spectral coefficients were calculated for one analysis window centered on the burst of C 1 and two subsequent windows covering the beginning of the aspiration phase. Findings confirmed that the combined use of four spectral coefficients is an effective method for detecting the anticipatory influence of a vowel on the initial burst of a preceding voiceless stop consonant. However, the observed patterns of anticipatory coarticulation showed no statistically significant differences, or trends toward such differences, between the stuttering and nonstuttering groups. Combining the present results for fluent speech in one given phonetic context with prior findings from both stuttered and fluent speech in a variety of other contexts, we conclude that there is currently no support for the hypothesis that the fluent speech of individuals who stutter is characterized by limited coarticulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".