Abstract
This paper presents a new application of the snap-drift algorithm [1]: feature discovery and clustering of speech waveforms from non-stammering and stammering speakers. The learning algorithm is an unsupervised version of snap-drift which employs the complementary concepts of fast, minimalist learning (snap) & slow drift (towards the input pattern) learning. The Snap-Drift Neural Network (SDNN) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, S.W., Palmer-Brown, D. (2006). Phonetic Feature Discovery in Speech Using Snap-Drift Learning. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_99
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DOI: https://doi.org/10.1007/11840930_99
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Print ISBN: 978-3-540-38871-5
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