Open AccessMeasuring (online) word segmentation in adults and children
Author(s) -
Iris Broedelet,
Paul Boersma,
Judith Rispens
Publication year - 2021
Publication title -
dutch journal of applied linguistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.205
H-Index - 7
eISSN - 2211-7253
pISSN - 2211-7245
DOI - 10.51751/dujal9607
Subject(s) - text segmentation , speech segmentation , segmentation , word (group theory) , syllable , statistical learning , psychology , test (biology) , speech recognition , natural language processing , cognition , computer science , cognitive psychology , artificial intelligence , linguistics , paleontology , philosophy , neuroscience , biology
Since Saffran, Aslin and Newport (1996) showed that infants were sensitive to transitional probabilities between syllables after being exposed to a few minutes of fluent speech, there has been ample research on statistical learning. Word segmentation studies usually test learning by making use of “offline methods” such as forced-choice tasks. However, cognitive factors besides statistical learning possibly influence performance on those tasks. The goal of the present study was to improve a method for measuring word segmentation online. Click sounds were added to the speech stream, both between words and within words. Stronger expectations for the next syllable within words as opposed to between words were expected to result in slower detection of clicks within words, revealing sensitivity to word boundaries. Unexpectedly, we did not find evidence for learning in multiple groups of adults and child participants. We discuss possible methodological factors that could have influenced our results.