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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Intra-Predictive Switched Split Vector Quantization of Speech Spectra

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Author(s):
Ramirez, Miguel Arjona [1]
Total Authors: 1
Affiliation:
[1] Univ Sao Paulo, Escola Politecn, Dept Elect Syst Engn, BR-05508 Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE SIGNAL PROCESSING LETTERS; v. 20, n. 8, p. 791-794, AUG 2013.
Web of Science Citations: 4
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-Loeve 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. (AU)

FAPESP's process: 12/24789-0 - Analysis of audio and speech signals for reconstruction and recognition
Grantee:Miguel Arjona Ramírez
Support Opportunities: Regular Research Grants