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A more flexible method for recognizing signals using back propagation: Piecewise linear regression vectors

  • Greg Makowski
Track 2: Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 507)

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.

Keywords

Speech Recognition Back Propagation Time Slice Test Pattern Dynamic Time Warping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Greg Makowski
    • 1
  1. 1.Computer Science DepartmentWestern Michigan UniversityKalamazoo

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