Optimal transformation of LSP parameters using neural network

Author

Vu, Hai Le ; Lois, Laszlo

Author_Institution

Dept. of Telecommun., Tech. Univ. Budapest, Hungary

Volume

2

fYear

1997

fDate

21-24 Apr 1997

Firstpage

1339

Abstract

The intraframe correlation properties of line spectrum pair (LSP) are used to develop an efficient encoding algorithm using the Karhunen-Loeve (KL) transformation. The important nonuniform statistical characteristics of LSP frequencies are investigated. Based upon this nonuniform property the neural network based techniques for generating the transform vectors via system training are studied. Using the principal component analysis (PCA) network to decorrelate LSP coefficients, we show that these new approaches lead to as good or better distortion as compared to other methods for speech analysis-synthesis

Keywords

correlation methods; neural nets; spectral analysis; speech coding; speech processing; speech synthesis; transform coding; transforms; Karhunen-Loeve transformation; LSP parameters; decorrelation; distortion; efficient encoding algorithm; intraframe correlation properties; line spectrum pair; low bit rate; neural network; nonuniform statistical characteristics; optimal transformation; principal component analysis network; speech analysis-synthesis; speech coding; system training; transform vectors; Encoding; Filters; Frequency; Karhunen-Loeve transforms; Linear predictive coding; Neural networks; Principal component analysis; Quantization; Speech analysis; Speech synthesis;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.596194

Filename

596194