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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Self-Organising Networks for Classification Learning from Normal and Aphasic Speech

Garfield, Sheila, Elshaw, Mark and Wermter, Stefan (2001) Self-Organising Networks for Classification Learning from Normal and Aphasic Speech. In: 23rd Conference of the Cognitive Science Society, 1-4 Aug 2001, University of Edinburgh, Edinbugh, Scotland, UK.

Item Type: Conference or Workshop Item (Paper)


An understanding of language processing in humans is critical if realistic computerised systems are to be
produced to perform various language operations. The examination of aphasia in individuals has provided a large
amount of information on the organisation of language processing, with particular reference to the regions in the
brain where processing occurs and the ability to regain language functionality despite damage to the brain. Given the importance played by aphasic studies an approach that can distinguish between aphasic forms was devised by using a
Kohonen self-organising network to classify sentences from the CAP (Comparative Aphasia Project) Corpus. We demonstrate that the different distributions of words in aphasics types may lead to grammatical systems which inhabit different areas in self-organising maps.

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Depositing User: Sheila Garfield


Item ID: 5690
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Catalogue record

Date Deposited: 26 Aug 2015 15:01
Last Modified: 02 Jul 2019 09:10


Author: Sheila Garfield
Author: Mark Elshaw
Author: Stefan Wermter

University Divisions

Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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