Isolated word recognition using dynamic synapse neural networks
Alireza Afshordi Dibazar, Hassan H. Namarvar, Theodore W. Berger · The Journal of the Acoustical Society of America · 2002
In this paper we propose a new method for using dynamic synapse neural networks (DSNNs) to accomplish isolated word recognition. The DSNNs developed by Liaw and Berger (1996) provide explicit analytic computational frameworks for the solution of nonlinear differential equations. Our method employs quasilinearization of a nonlinear differential equation to train a DSNN. This method employs an iterative algorithm, which converges monotonically to the extremal solutions of the nonlinear differential equation. The utility of the method was explored by training a simple DSNN to perform a speech recognition task on unprocessed, noisy raw waveforms of words spoken by multiple speakers. The simulation results showed that this training method has very fast convergence with respect to other existing methods. [Work supported by ONR and DARPA.]