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Title: Phonology impacts segmentation in online speech processing
Authors: Onnis, Luca
Monaghan, Padraic
Richmond, Korin
Chater, Nick
Issue Date: 2005
Citation: Journal of Memory and Language, 53(2):225-237, 2005.
Publisher: Elsevier
Abstract: Peña, Bonatti, Nespor and Mehler(2002) investigated an artificial language where the structure of words was determined by nonadjacent dependencies between syllables. They found that segmentation of continuous speech could proceed on the basis of these dependencies. However, Peña et al.'s artificial language contained a confound in terms of phonology, in that the dependent syllables began with plosives and the intervening syllables began with continuants. We consider three hypotheses concerning the role of phonology in speech segmentation in this task: (1) participants may recruit probabilistic phonotactic information from their native language to the artificial language learning task; (2) phonetic properties of the stimuli, such as the gaps that precede unvoiced plosives, can influence segmentation; and (3) grouping by phonological similarity between dependent syllables contributes to learning the dependency. In a series of experiments controlling the phonological and statistical structure of the language, we found that segmentation performance is influenced by the three factors in different degrees. Learning of non-adjacent dependencies did not occur when (3) is eliminated. We suggest that phonological processing provides a fundamental contribution to distributional analysis.
Keywords: artificial language learning
statistical learning
URI: doi:10.1016/j.jml.2005.02.011
Appears in Collections:CSTR publications

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