NDLI: Self-organization map of spiking neurons evaluation in phoneme classification

Content Provider	IEEE Xplore Digital Library
Author	Behi, T. Arous, N. Ellouze, N.
Copyright Year	2012
Description	Author affiliation: Electrical Engineering Department, National Engineering School of Tunis, Belvédère, Tunis 1002, Tunisia (Behi, T.; Arous, N.; Ellouze, N.)
Abstract	The Self-organizing Maps, proposed by Kohonen, have been used with a great deal of success in many applications. However, the basic SOM is designed to map patterns, or feature vectors, from an input space into an output space and does not take time into account. Speech recognition is a sequence of acoustic information and can't be considered as a static vector of information. In order to classify temporal sequences we present in this paper how to use spiking neurons for unsupervised competitive learning, preserving self-organizing maps. In fact we present two variants of SOM, the Leaky Integrators Neurons and the Spiking_SOM models. The Leaky Integrators Neurons model is based on the conservation of information, which makes it possible to consider the temporal order between the successive samples by using a mechanism called Leaky Integrators and the Spiking_SOM model preserves traditional model SOM, however it represents the characteristic to modify the learning function. The case study of the proposed models is phoneme classification in continuous speech.
Starting Page	701
Ending Page	705
File Size	331121
Page Count	5
File Format	PDF
ISBN	9781467316576
e-ISBN	9781467316583
DOI	10.1109/SETIT.2012.6481999
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-21
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Neurons Phoneme classification Kohonen map Speech recognition Spiking_SOM Educational institutions Speech Vectors Biological neural networks Leaky Integrators Neurons
Content Type	Text
Resource Type	Article

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