Neural Net Nonlinear Prediction for Speech Data

Document Type

Article

Publication Date

5-9-1991

Abstract

A new, nonlinear, neural network based, predictor has been devised for the encoding of speech data. It may be used in the design of a differential pulse code modulation (DPCM) coder for speech. A hybrid neural network architecture has been employed which combines the perceptron and back-propagation paradigms, thus called the PB-hybrid (PBH). Only two neurons are needed in the backpropagation section, keeping the required overhead modest. This predictor is designed by supervised training, based on a typical sequence of digitised values of samples in a speech frame. Simulation experiments have been carried out using 15 ms frames of 16 kHz speech data. The results obtained for the prediction gain show a 3 dB advantage of the PBH network over the linear predictor.© 1991, The Institution of Electrical Engineers. All rights reserved.

Identifier

0026420466 (Scopus)

Publication Title

Electronics Letters

External Full Text Location

https://doi.org/10.1049/el:19910517

ISSN

00135194

First Page

824

Last Page

826

Issue

10

Volume

27

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