A model of prenatal acquisition of speech parameters.

by: BS Seebach, N Intrator, P Lieberman, LN Cooper

Proceedings of the National Academy of Sciences of the United States of America, Vol. 91, No. 16. (2 August 1994), pp. 7473-7476.

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Abstract

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.

@article{citeulike:3035522, abstract = {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.}, address = {Department of Neuroscience, Brown University, Providence, RI 02912.}, author = {Seebach, B. S. and Intrator, N. and Lieberman, P. and Cooper, L. N. }, citeulike-article-id = {3035522}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {bcm, infant-speech-perception, neural-network, phonological-development, prenatal-development, speech-perception, unsupervised-learning}, month = {August}, number = {16}, pages = {7473--7476}, posted-at = {2008-07-23 01:20:34}, priority = {4}, title = {A model of prenatal acquisition of speech parameters.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/8052606}, volume = {91}, year = {1994} }

RIS record

TY - JOUR ID - citeulike:3035522 L3 - citeulike-article-id:3035522 TI - A model of prenatal acquisition of speech parameters. JF - Proceedings of the National Academy of Sciences of the United States of America VL - 91 IS - 16 SP - 7473 EP - 7476 AD - Department of Neuroscience, Brown University, Providence, RI 02912. SN - 0027-8424 N2 - 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. KW - bcm KW - infant-speech-perception KW - neural-network KW - phonological-development KW - prenatal-development KW - speech-perception KW - unsupervised-learning AU - Seebach, BS AU - Intrator, N AU - Lieberman, P AU - Cooper, LN PY - 1994/08/02/ UR - http://view.ncbi.nlm.nih.gov/pubmed/8052606 ER -

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