The current study provides a constraint-based analysis of L1 word-final consonant cluster acquisition in Turkish child language using some of L1 data originally presented by Topbas and Kopkalli-Yavuz (2008). The present analysis was done using [ɾ]+obstruent consonant cluster acquisition. A comparison of Gradual Learning Algorithm (GLA) under Optimality Theory (OT) as opposed to Harmonic Grammar (HG) is made to see under which model GLA functions more efficiently and reaches the target adult form faster. This convergence was simulated using the simulation feature of Praat (Boersma and Weenik, 2012). Since child language is unmarked at the initial state, faithfulness constraints have been assigned lower ranking values than markedness constraints. The noise was set to 2.0 and the plasticity to 0.1. The findings of the simulations show that GLA is more compatible with Noisy HG in showing convergence properties with the target adult output forms more effectively than OT-GLA does. In other words, the number of trial HG-GLA needed to reach the winning/optimal form was fewer than it was for OT-GLA.
First language acquisition, Gradual Learning Algorithm, Noisy Harmonic Grammar, sonorants, stochastic Optimality Theory, Turkish, word-final consonant clusters