  Many speech coders are based on linear prediction coding (LPC), nevertheless
with LPC is not possible to model the nonlinearities present in the speech
signal. Because of this there is a growing interest for nonlinear techniques.
In this paper we discuss ADPCM schemes with a nonlinear predictor based on
neural nets, which yields an increase of 1-2.5dB in the SEGSNR over classical
methods. This paper will discuss the block-adaptive and sample-adaptive
predictions.
