Non-Linear Adaptive Prediction of Speech with a Pipelined Recurrent Neural Network and Advanced Learning Algorithms
New learning algorithms for an adaptive non-linear forward predictor which is based on a Pipelined Recurrent Neural Network (PRNN) are presented. A computationally efficient Gradient Descent (GD) algorithm, as well as a novel Extended Recursive Least Squares (ERLS) algorithm are tested on the predictor. Simulation studies, based on three speech signals, which have been made public and are available on the World Wide Web (WWW), show that the non-linear predictor does not perform satisfactorily when the previously proposed gradient descent algorithm was used. The steepest descent algorithm is shown to yield a poor performance in terms of the prediction error gain, whereas consistently improved results are obtained using the ERLS algorithm. The merit of the non-linear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than only a linear structure predictor, which uses the conventional Recursive Least Squares (RLS) algorithm.
KeywordsSpeech Signal Extend Kalman Filter Little Mean Square Recurrent Neural Network Recursive Little Square
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