Abstract
Though vector quantizers are more efficient than scalar quantizers, their use for fine quantization of linear predictive coding (LPC) information (using 24-26 b/frame) is impeded due to their prohibitively high complexity. In the present work, a split vector quantization approach is used to overcome the complexity problem. The LPC vector, consisting of ten line spectral frequencies (LSFs), is divided into two parts and each part is quantized separately using vector quantization. Using the localized spectral sensitivity property of the LSF parameters, a weighted LSF distance measure is proposed. Using this distance measure, it is shown that the split vector quantizer can quantize LPC information in 24 b/frame with 1-dB average spectral distortion and <2% outlier frames (having spectral distortion greater than 2 dB).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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