Nonlinear Long-Term Prediction of Speech Signal
Summary :
This letter addresses a neural network (NN)-based predictor for the LP (Linear Prediction) residual. A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Preliminary results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.
- Publication
- IEICE TRANSACTIONS on Information Vol.E85-D No.8 pp.1346-1348
- Publication Date
- 2002/08/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Keyword
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Ki-Seung LEE, "Nonlinear Long-Term Prediction of Speech Signal" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 8, pp. 1346-1348, August 2002, doi: .
Abstract: This letter addresses a neural network (NN)-based predictor for the LP (Linear Prediction) residual. A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Preliminary results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_8_1346/_p
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@ARTICLE{e85-d_8_1346,
author={Ki-Seung LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Nonlinear Long-Term Prediction of Speech Signal},
year={2002},
volume={E85-D},
number={8},
pages={1346-1348},
abstract={This letter addresses a neural network (NN)-based predictor for the LP (Linear Prediction) residual. A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Preliminary results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Nonlinear Long-Term Prediction of Speech Signal
T2 - IEICE TRANSACTIONS on Information
SP - 1346
EP - 1348
AU - Ki-Seung LEE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2002
AB - This letter addresses a neural network (NN)-based predictor for the LP (Linear Prediction) residual. A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Preliminary results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.
ER -
English
