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Published on: July 13, 2019
Syllable onsets II: three-element clusters in phonological treatment
1Department of Speech and Hearing Sciences, Indiana University, Bloomington 47405-7002, USA. gierut@indiana.edu
Children with speech sound disorders learned complex three-element consonant clusters during treatment. However, generalization effects varied, impacting singleton and two-element clusters differently based on individual sound inventories.
Area of Science:
- Linguistics
- Speech-Language Pathology
- Developmental Psychology
Background:
- The Sonority Sequencing Principle (SSP) guides typical phonological development.
- Word-initial three-element clusters present unique linguistic challenges due to representational asymmetry.
- The efficacy of treating complex clusters in children with phonological delays is not well understood.
Purpose of the Study:
- To investigate the acquisition of word-initial three-element clusters in children with functional phonological delays.
- To examine the impact of treatment on complex cluster acquisition and generalization patterns.
- To explore the relationship between linguistic complexity, treatment effectiveness, and generalization.
Main Methods:
- A single-subject staggered multiple-baseline experimental design was employed.
- Treatment focused on word-initial three-element clusters for children aged 3;4 to 6;3.
- Generalization to untreated singletons and two-element clusters was assessed.
Main Results:
- Children successfully learned the treated three-element clusters.
- No generalization occurred to similar asymmetric onsets.
- Widespread generalization to untreated singletons (including affricates) was observed.
- Differential generalization to two-element clusters was linked to individual singleton inventories.
Conclusions:
- Treatment of complex three-element clusters can be effective for children with phonological delays.
- Generalization patterns are complex and influenced by linguistic factors like inventory composition.
- Findings suggest a segmental-syllabic interface model for predicting treatment outcomes in speech sound disorders.
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