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This paper describes an approach to the optimization of the nonlinear component of a physiologically motivated feature extraction system for automatic speech recognition. Most computational models of the peripheral auditory system include a sigmoidal nonlinear function that relates the log of signal intensity to output level, which we represent by a set of frequency dependent logistic functions. The...
This paper describes ways of speeding up the optimization process for learning physiologically-motivated components of a feature computation module directly from data. During training, word lattices generated by the speech decoder and conjugate gradient descent were included to train the parameters of logistic functions in a fashion that maximizes the a posteriori probability of the correct class...
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