Phonetic Feature Discovery in Speech using Snap-Drift

Conference paper

Lee, S. and Palmer-Brown, Dominic 2006. Phonetic Feature Discovery in Speech using Snap-Drift.
AuthorsLee, S. and Palmer-Brown, Dominic
TypeConference paper
Abstract

This paper presents a new application of the snapdrift
algorithm [1]:
feature discovery and clustering of speech waveforms from nonstammering
and stammering speakers. The learning algorithm is an unsupervised version of
snapdrift
which employs the complementary concepts of fast, minimalist
learning (snap) & slow drift (towards the input pattern) learning. The SnapDrift
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.

Keywordsalgorithms; Speech disorders; computer phonetic interpretation
Year2006
Accepted author manuscript
License
CC BY-ND
Publication dates
PrintSep 2006
Publication process dates
Deposited29 Apr 2010
ISSN0302-9743
1611-3349
Web address (URL)http://dx.doi.org/10.1007/11840930_99
http://hdl.handle.net/10552/767
Additional information

Citation:
Lee, S. W. and Palmer-Brown, D. (2006). "Phonetic Feature Discovery in Speech using Snap-Drift." International Conference on Artificial Neural Networks (ICANN'2006) (Athen, Greece, 10th - 14th September 2006), S. Kollias et al. (Eds.): ICANN 2006, Part II, LNCS 4132, pp. 952 -962..

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Accepted author manuscript
Lee, S.W. (2006) ICANN 952-62.pdf
License: CC BY-ND

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