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In the present paper we use a range of modeling techniques to investigatewhether an abstract phone could emerge from exposure to speech sounds. Ineffect, the study represents an attempt for operationalize a theoretical deviceof Usage-based Linguistics of emergence of an abstraction from language use.Our quest focuses on the simplest of such hypothesized abstractions. We testtwo opposing principles regarding the development of language knowledge inlinguistically untrained language users: Memory-Based Learning (MBL) andError-Correction Learning (ECL). A process of generalization underlies theabstractions linguists operate with, and we probed whether MBL and ECL couldgive rise to a type of language knowledge that resembles linguisticabstractions. Each model was presented with a significant amount ofpre-processed speech produced by one speaker. We assessed the consistency orstability of what these simple models have learned and their ability to giverise to abstract categories. Both types of models fare differently with regardto these tests. We show that ECL models can learn abstractions and that atleast part of the phone inventory and grouping into traditional types can bereliably identified from the input.

A learning perspective on the emergence of abstractions: the curious case of phonemes