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Previous language learning research reveals that the statistical properties of the input offer sufficient information to allow listeners to segment words from fluent speech in an artificial language. The current pair of studies uses a natural language to test the ecological validity of these findings and to determine whether a listener's language background influences this process. In Study 1, the “guessibility” of potential test words from the Norwegian language was presented to 22 listeners who were asked to differentiate between true words and nonwords. In Study 2, 22 adults who spoke one of 12 different primary languages learned to segment words from continuous speech in an implicit language learning paradigm. The task consisted of two sessions, approximately three weeks apart, each requiring participants to listen to 7.2 minutes of Norwegian sentences followed by a series of bisyllabic test items presented in isolation. The participants differentially accepted the Norwegian words and Norwegian-like nonwords in both test sessions, demonstrating the capability to segment true words from running speech. The results were consistent across three broadly-defined language groups, despite differences in participants’ language background.
