Fast LSP vector quantization algorithms comparison

The line spectrum pairs (LSP) provide an efficient representation of the synthesis filter used in linear predictive coding of speech. In this paper, an attempt to find the best distance measure for vector quantization is carried out, in the first place, by making objective studies over the same training sequence. Lastly, fast VQ algorithms of the LSP parameters are compared in terms of complexity, using the Euclidean distance measure. The well-known ordering property of LSP parameters is exploited to improve the efficiency of minimum distortion encoder for VQ in terms of norm associated to its distance. As conventional full search is too complex for practical implementation, the originality of this work consists in using the norm to limit the size of the area which contains the nearest neighbor of an input vector to he quantized. This method results in a substantial reduction in search complexity with only a minor degradation in terms of average spectral distortion