Speech coding through adaptive combined nonlinear prediction

In this paper we propose a nonlinear predictive speech encoder based on an adaptive combiner with a neural net that weighs the prediction of several nonlinear predictors. Thus, we exploit the advantages of data fusion on a nonlinear prediction scheme, where it appears in a more natural way than for linear predictors. Experimental results reveal that this scheme outperforms the fixed combination (with mean, median, etc. operators) up to 1.5 dB in SEGSNR.