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In A. Sorace, C. Heycock & R. Shillcock (Eds.), Proceedings of
GALA 1997: Language Acquisition: Knowledge Representation and
Processing (pp. 327-332). University of Edinburgh.
Coping with Variation in Speech Segmentation
Morten H. Christiansen & Joe Allen
Abstract
This paper presents results from word segmentation simulations in
which a Simple Recurrent Network (SRN) was exposed to speech input
incorporating high degrees of variation. In Experiment 1, a network
trained on a speech corpus transcribed to include variation in terms
of coarticulation was compared with a network trained on a citation
form version of the same corpus. The results show that the network
accommodates this variation without significant impairment to its
performance on the segmentation task. Experiment 2 involved a novel
approach to the modeling of segmental variation in which feature
values were systematically varied according to a predetermined
probability schedule. Results demonstrate that following training the
networks were able to withstand a very high degree of segmental
variation within words and still able to locate word boundaries in the
input. Together the experiments indicate that the SRN provides a
robust mechanism for the modeling of early speech segmentation.
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