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Gaussian Mixture Model Based Switched Split Vector Quantization of LSF Parameters

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2 Author(s)
Saikat Chatterjee ; Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India 560012, Email: saikat@ece.iisc.ernet.in ; T. V. Sreenivas

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Published in:

2007 IEEE International Symposium on Signal Processing and Information Technology

Date of Conference:

15-18 Dec. 2007