Intra-Predictive Switched Split Vector Quantization of Speech Spectra

Author

Ramirez, M.A.

Author_Institution

Dept. of Electron. Syst. Eng. in Escola Politec., Univ. of Sao Paulo, Sao Paulo, Brazil

Volume

20

Issue

8

fYear

2013

fDate

Aug. 2013

Firstpage

791

Lastpage

794

Abstract

Vector quantization (VQ) of speech spectral vectors has been improved by techniques such as split VQ (SVQ), vector transforms and direction switching. This letter proposes Intra-Predictive Switched SVQ (IPSSVQ) with direction switching by a Gaussian Mixture Model (GMM), using at the frame level the prediction-based lower-triangular transform (PLT), which has lower complexity than the Karhunen-Loève transform (KLT). It is shown that equivalent results to GMM KLT SSVQ may be obtained in the quantization of line spectral frequency (LSF) vectors from wideband speech signals, such as transparent coding throughout the range from 46 bit/frame to 41 bit/frame, with about three-fourths as much operational complexity.

Keywords

Gaussian processes; speech processing; transforms; GMM; Gaussian mixture model; IPSSVQ; KLT; Karhunen-Loève transform; LSF vectors; PLT; direction switching; intra predictive switched SVQ; intrapredictive switched split vector quantization; line spectral frequency; operational complexity; prediction based lower triangular transform; speech spectral vectors; split VQ; transparent coding; vector transforms; wideband speech signals; Complexity theory; Speech; Switches; Transforms; Vector quantization; Vectors; Karhunen-Loève transform; Vector quantization; intra-predictive quantization; line spectral frequencies; prediction-based lower-triangular transform;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2013.2267391

Filename

6527953