Word spotting with the gamma neural model

Craig Fancourt; Neil Euliano; Jose C. Principe

doi:10.1117/12.205183

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.

Citation Download Citation

Craig Fancourt, Craig Fancourt, Neil Euliano, Neil Euliano, Jose C. Principe, Jose C. Principe, } "Word spotting with the gamma neural model", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205183; https://doi.org/10.1117/12.205183

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