The Effects of Self-Disclosure on Male and Female Perceptions of Individuals Who Stutter
Abstract
Purpose: The purpose of this study was to examine the influence of self-disclosure on observers' perceptions of persons who stutter. Method: Participants ( N = 173) were randomly assigned to view 2 of 4 possible videos (i.e., male self-disclosure, male no self-disclosure, female self-disclosure, and female no self-disclosure). After viewing both videos, participants completed a survey assessing their perceptions of the speakers. Results: Controlling for observer and speaker gender, listeners were more likely to select speakers who self-disclosed their stuttering as more friendly, outgoing, and confident compared with speakers who did not self-disclose. Observers were more likely to select speakers who did not self-disclose as unfriendly and shy compared with speakers who used a self-disclosure statement. Controlling for self-disclosure and observer gender, observers were less likely to choose the female speaker as friendlier, outgoing, and confident compared with the male speaker. Observers also were more likely to select the female speaker as unfriendly, shy, unintelligent, and insecure compared with the male speaker and were more likely to report that they were more distracted when viewing the videos. Conclusion: Results lend support to the effectiveness of self-disclosure as a technique that persons who stutter can use to positively influence the perceptions of listeners.
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As PWS participants imagined the use of their annotated speech, the interpretation of labels is never final but continuously evolving; for example, inaccurate annotations of stuttering type might still be useful in helping conversation partners become aware of the existence of stuttering, which could enhance listeners' comprehension of stuttered speech.
“As PWS participants imagined the use of their annotated speech, the interpretation of labels is never final but continuously evolving; for example, inaccurate annotations of stuttering type might still be useful in helping conversation partners become aware of the existence of stuttering, which could enhance listeners' comprehension of stuttered speech [15].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [15]
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- Authors
- Byrd, C. T., McGill, M., Gkalitsiou, Z., & Cappellini, C.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- American Journal of Speech-Language Pathology
- Published
- 2017-02
- Volume
- 26
- Issue
- 1
- Pages
- 69-80
- ISSN
- 1058-0360 (ISSN-L)
- 1558-9110
- OpenAlex
- W2569951386
- Cited by
- 75
- References
- 35
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- FWCI
- 6.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Speech-Language Pathology (publishedVersion)
- PubMed (publishedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Stuttering Research and Treatment
- Social Sciences › Psychology › Social Psychology › Communication in Education and Healthcare
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- stuttering, self-disclosure, social perception, viewer perception, gender differences, person perception
- MeSH terms
- Adolescent, Adult, Attention, Attitude, Female, Gender Identity, Humans, Intelligence, Male, Prejudice, Public Opinion, Shyness, Social Behavior, Social Perception, Stereotyping, Students, Stuttering, Video Recording, Young Adult, Self Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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