Journal of speech, language, and hearing research : JSLHR · 2026
Linguistically Informed Automated Estimates of Creak in Adductor Laryngeal Dystonia.
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Fletcher Brittany N, Van Doren Maxine, Kapsner-Smith Mara R, Sauder Cara, Eadie Tanya L, Marks Katherine L, Toles Laura E, Stepp Cara E, Vojtech Jenny
Аннотация
PURPOSE: Creaky voice is a perceptually low-pitched and irregular voice quality that has shown promise as an objective marker of adductor laryngeal dystonia (AdLD). However, creak also occurs in mainstream American English through linguistic processes (i.e., phrase-final creak, /t/ glottalization, word-initial vowel glottalization). This study investigated whether linguistically unexpected creak (i.e., creak occurring outside these phonological contexts) offers improved differentiation between AdLD and typical voices compared with total creak.
METHOD: Fifty speakers with AdLD and 50 age- and sex-matched controls read the first paragraph of The Rainbow Passage. A priori locations for linguistically expected creak were identified based on established phonological processes. An automated algorithm detected the percentage of creak (%creak) within voiced segments. A linear mixed-effects model examined the interaction between group (AdLD, control) and creak type (expected vs. unexpected). Receiver operating characteristic curve analyses compared the diagnostic accuracy of unexpected %creak and total %creak.
RESULTS: Both groups produced more expected than unexpected creak, but a significant interaction revealed that this difference was statistically weaker in the AdLD group. Between-groups differences were significantly larger for unexpected creak than for expected creak. Unexpected creak demonstrated superior diagnostic accuracy (area under the curve = .82 vs. .77, p < .001), with an optimal cutoff of 4.0% at a sensitivity of .78 and a specificity of .84.
CONCLUSIONS: Pathological creak in AdLD disrupts normal linguistic patterning, spreading disproportionately into unexpected contexts. Linguistically unexpected creak provides superior diagnostic discrimination compared with total creak, suggesting that acoustic biomarkers accounting for linguistic context can meaningfully enhance diagnostic accuracy for AdLD.
METHOD: Fifty speakers with AdLD and 50 age- and sex-matched controls read the first paragraph of The Rainbow Passage. A priori locations for linguistically expected creak were identified based on established phonological processes. An automated algorithm detected the percentage of creak (%creak) within voiced segments. A linear mixed-effects model examined the interaction between group (AdLD, control) and creak type (expected vs. unexpected). Receiver operating characteristic curve analyses compared the diagnostic accuracy of unexpected %creak and total %creak.
RESULTS: Both groups produced more expected than unexpected creak, but a significant interaction revealed that this difference was statistically weaker in the AdLD group. Between-groups differences were significantly larger for unexpected creak than for expected creak. Unexpected creak demonstrated superior diagnostic accuracy (area under the curve = .82 vs. .77, p < .001), with an optimal cutoff of 4.0% at a sensitivity of .78 and a specificity of .84.
CONCLUSIONS: Pathological creak in AdLD disrupts normal linguistic patterning, spreading disproportionately into unexpected contexts. Linguistically unexpected creak provides superior diagnostic discrimination compared with total creak, suggesting that acoustic biomarkers accounting for linguistic context can meaningfully enhance diagnostic accuracy for AdLD.