Open AccessA model of prenatal acquisition of speech parameters.
Author(s) -
Bradley S. Seebach,
Nathan Intrator,
Phil Lieberman,
Leon N. Cooper
Publication year - 1994
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.91.16.7473
Subject(s) - perception , computer science , speech perception , artificial neural network , psychology , speech recognition , linguistics , natural language processing , artificial intelligence , cognitive psychology , neuroscience , philosophy
An unsupervised neural network model inductively acquires the ability to distinguish categorically the stop consonants of English, in a manner consistent with prenatal and early postnatal auditory experience, and without reference to any specialized knowledge of linguistic structure or the properties of speech. This argues against the common assumption that linguistic knowledge, and speech perception in particular, cannot be learned and must therefore be innately specified.