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Proceedings Paper

Word spotting with the gamma neural model
Author(s): Craig Fancourt; Neil Euliano; Jose C. Principe
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Paper Abstract

This paper discusses the application of the gamma neural model to word spotting. The gamma model is a dynamic neural model where the conventional tap delay line of the TDNN is replaced by a local recursive memory structure. This model is able to find the best memory depth for a given processing task when the number of taps in the memory is specified. It can also compensate for time warping. In our approach, word spotting is the detection of a signature (the keyword under analysis) in a noisy background (other words of continuous speech). Unlike other approaches, we do not segment the input, and the neural net learns over time how to recognize the patterns associated with a given word. We test two gamma model topologies for their sensitivity to time warping and amplitude variations.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205183
Show Author Affiliations
Craig Fancourt, Univ. of Florida (United States)
Neil Euliano, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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