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
