Predicting Stuttering Severity Ratings by Timing and Tallying Dysfluencies Using Praat Software.
Purpose: The goal of this study was to examine the relationship between objective descriptors of stuttering behavior and perceptions of stuttering severity. Classification systems for speech dys-fluencies are numerous; this study sought to find a less complicated, yet accurate, predictor of stutteri...
| Publicado en: | Contemporary Issues in Communication Science & Disorders Vol. 43; pp. 106 - 115 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
National Student Speech Language Hearing Association
Spring2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115260161&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115260161 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10925171 1VOM jtl: Contemporary Issues in Communication Science & Disorders issn: 10925171 maglogo: N pubinfo: dt: Spring2016 vid: 43 pid: 27617 pub: National Student Speech Language Hearing Association place: Rockville, Maryland artinfo: ui: 115260161 115260161 115260161 10.1044/cicsd_43_s_106 115260161 ppf: 106 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Predicting Stuttering Severity Ratings by Timing and Tallying Dysfluencies Using Praat Software. aug: au: Hasseltine, Elizabeth S. Black, Shannon F. Corcoran, Tayler M. DiPalma, Danika L. Dixon, Susan E. Gooch, Anne T. Hurlburt, Lauren M. Murray, Ashton B. Potts, Kathryn B. Schnizler, Anna C. Secrist, Caitlin Shickel, Roma Marisa Loncke, Filip Corthals, Paul affil: University of Virginia, Charlottesville sug: subj: Fluency Disorders Severity of Illness Evaluation Software Utilization Human Classification Regression Correlational Studies Summated Rating Scaling Data Analysis Software Pearson's Correlation Coefficient ab: Purpose: The goal of this study was to examine the relationship between objective descriptors of stuttering behavior and perceptions of stuttering severity. Classification systems for speech dys-fluencies are numerous; this study sought to find a less complicated, yet accurate, predictor of stuttering severity. Method: This study used a taxonomy of stutters outlined by Teesson, Packman, and Onslow (2003). Using this taxonomy, recorded speech samples were annotated using Praat software, and the type of stuttering symptom and its duration was determined. The power of dysfluency types and duration as predictors for stuttering severity was examined by means of a regression analysis. Raters evaluated 1-min speech samples for perceived stuttering severity. Results: Timing parameters yielded more significant severity predictors than tallying parameters did. A concise equation for predicting stuttering severity was established that accounted for the duration of fluent speech in conversation. Conclusion: Although the timing parameter predictor of objective stuttering severity is only 1 component of the assessment of a person who stutters, this simplified focus on percentage of time fluent will compensate for the lack of consensus regarding stuttering classification systems. These quantitative data can be used in addition to an assessment of a person's stuttering experience to better understand and treat fluency disorders holistically (Yaruss & Quesal, 2006). pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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