ArticleThe Laryngoscope2026
Automated Creak Identifies Laryngeal Dystonia During Conversational Speech.
Article in The Laryngoscope, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Creak Derived from CAPE-V Sentences in Patients with AdLd and pMTD.Journal of voice : official journal of the Voice Foundation · 2026Article
- Automated Creak Identifies Laryngeal Dystonia During Conversational Speech.The Laryngoscope · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
objectiveCreak is an acoustic feature that serves an umbrella term for irregularities in fundamental frequency. This study evaluated whether automated creak distinguished speakers with adductor laryngeal dystonia (AdLD), primary muscle tension dysphonia (pMTD), and those without voice disorders during conversational speech.
methodsTwo stimulus types (reading passage and conversational speech) were produced by 50 speakers with AdLD, 50 speakers with pMTD, and 50 control speakers. An automated creak detector was used to calculate the percentage (%) of creak in both stimuli. The effects of group, stimulus type, and their interaction on % creak were assessed. Receiver operating characteristic curve analyses were conducted and the area under the curve (AUC) was used to evaluate the effectiveness of the creak detector in distinguishing among groups.
resultsWe found a statistically significant effect of group on % creak, but no effect of stimulus type on % creak, suggesting that creak distinguished speakers with AdLD, pMTD, and controls during both a reading passage and conversational speech. AUC values indicated acceptable discriminative ability in distinguishing AdLD speakers from both pMTD speakers and controls across both stimuli types.
conclusionAutomated creak demonstrates promise as a discriminative feature for identifying differences between speakers with AdLD from pMTD and controls in both a reading passage and conversational speech. LEVEL OF EVIDENCE: 3:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.