Recognising Embedded Words in Connected Speech:
Context and Competition
in Proceedings of the Fourth Neural Computation and Psychology
Workshop
J. A. Bullinaria, D. W. Glasspool, G. Houghton (Eds). London: Springer-Verlag
April 1997
Matt H. Davis, William D. Marslen-Wilson and M. Gareth Gaskell
E-mail: matt.davis@mrc-cbu.cam.ac.uk
Onset-embedded words (e.g. cap in captain) present a problem for accounts
of spoken word recognition since information coming after the offset of
the embedded word may be required for identification. We demonstrate that
training a simple recurrent network to activate a representation of all
the words in a sequence allows the network to learn to recognise onset-embedded
words without requiring a training set that is already lexically segmented.
We discuss the relationship between our model and other accounts of lexical
segmentation and word recognition, and compare the model’s performance
to psycholinguistic data on the recognition of onset-embedded words.