Coding of variable dimension speech spectral vectors using weighted nonsquare transform vector quantization

Abstract

This paper addresses the problem associated with variable-dimension vector quantization, and presents a new quantization technique that combines a variable-size nonsquare transform (NST) with a fixed-dimension vector quantizer. We show that all linear dimension conversion methods can be treated as special cases of a general approach for linear dimension conversion formulated as NST. By incorporating the speech perceptual properties, we introduce a technique called weighted nonsquare transform vector quantization (WNSTVQ) for the quantization of speech spectral vectors. We show that the total perceptual weighted distortion can be separated into the weighted modeling distortion, which is solely determined by the choice of transforms in WNSTVQ and the weighted quantizer distortion. We discuss the factors that influence the performance of the WNSTVQ system and provide a complexity analysis for two WNSTVQ implementations. Finally, experimental results are presented to show that the WNSTVQ system has the ability to trade performance for computational complexity and memory storage by selecting suitable transforms and/or the length of fixed-dimension vectors.