How good is your β?-observations on VQ training ratios

A growing number of state of the art speech coding algorithms use vector quantization (VQ) to quantize spectrum information. VQ code books are created from a set of training vectors which are drawn from and representative of the overall data being quantized. These training vectors are partitioned into a set of clusters whose centroids represent the region of the partition and are called code vectors. Of specific interest to this paper is the ratio, β, of the number of training vectors to the number of code vectors. The goal is to provide guidance on appropriate levels of training data regardless of code book size. Of particular significance is the empirical determination of a minimum β value of 128 training vectors per code vector for full vector code books