In the last years there has been a growing interest for nonlinear speech models. Several works have been published revealing the better performance of nonlinear techniques, but little attention has been dedicated to the implementation of the nonlinear model into real applications. This work is focused on the study of the behaviour of a nonlinear predictive model based on neural nets, in a speech waveform coder. Our novel scheme obtains an improvement in SEGSNR between 1 and 2 dB for an adaptive quantization ranging from 2 to 5 bits.

This paper is focused on nonlinear prediction coding, which consists on the prediction of a speech sample based on a nonlinear combination of previous samples. It is known that in the generation of the glottal pulse, the wave equation does not behave linearly [2], [10], and we model these effects by means of a nonlinear prediction of speech based on a parametric neural network model. This work is centred on the neural net weight's quantization and on the compression gain.

In his article Vallverdu traces the history of the Catalan language from its origins to the present day, in a sociolinguistic perspective. He devotes special attention to the situation since the mid-19th century. The article stresses the fact that the effects of the savage repression carried out by the Franco regime against the Catalan language have yet to be fully overcome, and that the present situation does not justify the unqualified optimism expressed in certain quarters as regards the prospects for a full recovery of the use of Catalan.

In this work we present a waveform speech c~ ding system including vector quantization. This system can be seen as a vector version of the scalar ADPCM speech coder. In such system the speech samples are grouped in vectors that are coded by a vector quantizer when its prediction subtraction has been made. This is obtained in the coder by a vector Due to the non-statio narity of the speech signal, the code must be continuously adapted to the local characteristics of the current input signal of the speech. We propose the use of an adaptive vector predictor that follows the speech statistics variations so the,prediction error to be coded presents the minimum dynamic range. On the other side it is wellkno~m that the prediction error is proportional to the signal energy. To compensate this effect a vector quantizer "gain-shape" model has been proposed, so vectors gain and its shape are separately coded. The obtained empirical results are very promising and exhibit good competitivity with other solutions existing in the literature.

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