Articulatory and segmental performance in children with and without speech disorder: A multiple case pilot study.

Clinical linguistics & phonetics(2022)

引用 3|浏览0
暂无评分
摘要
This multiple case pilot study explored how nonword imitation influences articulatory and segmental performance in children with and without speech disorder. Eight children, ages 4- to 8-years-old, participated, including two children with childhood apraxia of speech (CAS), four children with phonological disorder (PD), and two children with typical development (TD). Tokens included two complexity types and were presented in random order. Minimal feedback was provided and nonwords were never associated with a referent. Kinematic and transcription data were analysed to examine articulatory variability, segmental accuracy, and segmental variability in session 1 and session 5. Descriptive statistics, percent change, effect sizes, and Pearson correlations are reported. In session 1, the two participants with CAS showed high articulatory variability, low segmental accuracy, and high segmental variability compared to the participants with PD and TD. By session 5, both participants with CAS, two with PD, and one with TD showed increased articulatory variability in the lowest complexity nonword. Segmental accuracy remained low and variability remained high for the two participants with CAS in session 5, whereas several participants with PD and TD showed improved segmental performance. Articulatory and segmental variability were not significantly correlated. The results of this study suggest that motor practice with minimal feedback and no assignment of a lexical referent can instantiate positive changes to segmental performance for children without apraxia. Positive changes to segmental performance are not necessarily related to increased articulatory control; these two processing levels can show distinct and disparate learning trajectories.
更多
查看译文
关键词
Speech motor control,childhood apraxia of speech,phonological disorder,segmental variability,speech motor learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要