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6 April 1995 Word spotting with the gamma neural model
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig Fancourt, Neil Euliano, and Jose C. Principe "Word spotting with the gamma neural model", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995);


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