Abstract
Using the neural network architecture of back propagation applied to speech recognition, a new input data structure is developed which improves shift invariance when recognizing signal data in the form such as a formant. The preliminary development of the data structure, piecewise linear regression vectors, is reported. The new input data structure reduces the amount of data presented to the network by as much as an order of magnitude, giving a computational advantage in execution speed.
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© 1991 Springer-Verlag Berlin Heidelberg
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Makowski, G. (1991). A more flexible method for recognizing signals using back propagation: Piecewise linear regression vectors. In: Sherwani, N.A., de Doncker, E., Kapenga, J.A. (eds) Computing in the 90's. Great Lakes CS 1989. Lecture Notes in Computer Science, vol 507. Springer, New York, NY. https://doi.org/10.1007/BFb0038480
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DOI: https://doi.org/10.1007/BFb0038480
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