Sequential gradient estimation predictor for speech signals

A sequential gradient estimation predictor [SGEP] for speech signals is presented. In a given sampling interval, each prediction coefficient in turn is increased and decreased in value by a prescribed amount, while the other coefficients are kept constant. Two predictions are then made and the better enables the coefficient to be modified in the correct direction, but by an amount determined by a number of factors. The superiority of SGEP over the stochastic approximation predictor is illustrated by waveforms and snr (signal to noise ratio) performance curves.