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In this paper we compare three different implementations of language learning to investigate the issue of speaker-dependent initial representations and subsequent generalization. These implementations are used in a comprehensive model of language acquisition under development in the FP6 FET project ACORNS. All algorithms are embedded in a cognitively and ecologically plausible framework, and perform the task of detecting word-like units without any lexical, phonetic, or phonological information. The results show that the computational approaches differ with respect to the extent they deal with unseen speakers, and how generalization depends on the variation observed during training.