Courtney T Byrd1, Geoffrey A Coalson2, Jie Yang3, Kirsten Moriarty4. 1. The University of Texas at Austin, 1 University Station A1100, Austin, TX 78759, USA. Electronic address: courtney.byrd@austin.utexas.edu. 2. Louisiana State University, 81 Hatcher Hall, Baton Rouge, LA 70810, USA. Electronic address: gcoals1@lsu.edu. 3. The University of Texas at Austin, 1 University Station A1100, Austin, TX 78759, USA. Electronic address: thyjessie@gmail.com. 4. The University of Texas at Austin, 1 University Station A1100, Austin, TX 78759, USA. Electronic address: kirstenmoriarty@yahoo.com.
Abstract
PURPOSE: The purpose of this study was to investigate the influence of phonetic complexity as measured by the Word Complexity Measure (WCM) on the speed of single-word production in adults who do (AWS, n=15) and do not stutter (AWNS, n=15). METHOD: Participants were required to name pictures of high versus low phonetic complexity and balanced for lexical properties. Speech reaction time was recorded from initial presentation of the picture to verbal response of participant for each word type. Accuracy and fluency were manually coded for each production. RESULTS: AWS named pictures significantly slower than AWNS, but there were no significant differences observed in response latency when producing word of high versus low phonetic complexity as measured by the WCM. CONCLUSION: Findings corroborate past research of overall slowed picture naming latencies in AWS, compared to AWNS. Findings conflict with data that suggest that the phonetic complexity of words uniquely compromises the speed of production in AWS. The potential interaction between lexical and phonetic factors on single-word production within each group are discussed.
PURPOSE: The purpose of this study was to investigate the influence of phonetic complexity as measured by the Word Complexity Measure (WCM) on the speed of single-word production in adults who do (AWS, n=15) and do not stutter (AWNS, n=15). METHOD:Participants were required to name pictures of high versus low phonetic complexity and balanced for lexical properties. Speech reaction time was recorded from initial presentation of the picture to verbal response of participant for each word type. Accuracy and fluency were manually coded for each production. RESULTS:AWS named pictures significantly slower than AWNS, but there were no significant differences observed in response latency when producing word of high versus low phonetic complexity as measured by the WCM. CONCLUSION: Findings corroborate past research of overall slowed picture naming latencies in AWS, compared to AWNS. Findings conflict with data that suggest that the phonetic complexity of words uniquely compromises the speed of production in AWS. The potential interaction between lexical and phonetic factors on single-word production within each group are discussed.
Authors: Vikram N Dayalu; Joseph Kalinowski; Andrew Stuart; Donald Holbert; Michael P Rastatter Journal: J Speech Lang Hear Res Date: 2002-10 Impact factor: 2.297