ASA 128th Meeting - Austin, Texas - 1994 Nov 28 .. Dec 02

1pSP11. Self-organizing maps of stop consonant place from token-level locus equation inputs.

Jon Hilbert

Dept. of Comput. Sci., Univ. of Texas, Austin, TX 78712

David E. Fruchter

Mona McWilliams

Joseph Sirosh

Harvey M. Sussman

Univ. of Texas, Austin, TX 78712

Previously [D. E. Fruchter, J. Acoust. Soc. Am. 95, 2977 (1994)], identification curves were estimated for English /b,d,g/ using synthetic CV stimuli comprehensively sampling the F2-onset X F2-vowel acoustic space in the vicinity of Sussman's /b,d,g/ locus equations. These results were used to delineate ``identification surfaces'' situated in locus equation space. The current research uses a biologically plausible neural network (the Kohonen algorithm) to model the above perception results. This algorithm is an abstraction of the local, unsupervised map-organizing process thought to occur in the brain. The Kohonen map forms a two-dimensional representation of stop consonant place categories from F2-onset and F2-vowel inputs. This emergent representation corresponds well with the experimentally observed identification surfaces and can be used to classify novel inputs and predict phoneme boundaries and confusability regions.