Consequences of phonological variation for algorithmic word segmentation.

ABSTRACT: Over the first year, infants begin to learn the words of their language. Previous work suggests that certain statistical regularities in speech could help infants segment the speech stream into words, thereby forming a proto-lexicon that could support learning of the eventual vocabulary. However, computational models of word segmentation have typically been tested using language input that is much less variable than actual speech is. We show that using actual, transcribed pronunciations rather than dictionary pronunciations of the same speech leads to worse segmentation performance across models. We also find that phonologically variable input poses serious problems for lexicon building, because even correctly segmented word forms exhibit a complex, many-to-many relationship with speakers'

SUBMITTER: Beech C 

PROVIDER: S-EPMC10085835 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature