Feature discovery using snap-drift neural networks

Conference paper

Lee, S. and Palmer-Brown, Dominic 2007. Feature discovery using snap-drift neural networks. Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference University of East London pp. 61-70
AuthorsLee, S. and Palmer-Brown, Dominic
TypeConference paper
Abstract

This paper introduces an application of Snap-Drift Neural Networks (SDNNs), which
employs the complementary concepts of fast, minimalist (snap) learning and slow (drift towards the
input pattern) learning, for feature discovery and classification of speech waveforms from nonstammering
and stammering speakers. The speech waveforms are drawn from a phonetically
annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by
the SDNN. The results show that SDNN groups the phonetics speech input patterns meaningfully
and extracts properties which are common to both non-stammering and stammering speech, as well
as distinct features that are common within each of the utterance groups, thus supporting
classification. SDNN is also being applied in a virtual learning environment to categorise students’
test responses and thereby support individualised feedback.

KeywordsSnap-Drift Neural Networks; classification of speech waveforms; phonetic interpretation; virtual learning environment
Year2007
ConferenceProceedings of Advances in Computing and Technology
Publisher's version
License
CC BY-ND
Publication dates
Print2007
Publication process dates
Deposited19 Jul 2010
Web address (URL)http://www.uel.ac.uk/act/proceedings/documents/ACT06Proceeding.pdf
http://hdl.handle.net/10552/870
Additional information

Citation:
Lee, S.W.; Palmer-Brown, D. (2007) ‘Feature discovery using snap-drift neural networks’ Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 2nd Annual Conference, University of East London, pp.61-70.

Place of publicationUniversity of East London
Page range61-70
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