DESCRIPTION LPC VECTOR QUANTIZATION APPARATUS Technical Field The present invention relates to an LPC vector quantization apparatus applicable to a speech coder/decoder used to enhance transmission efficiency of a speech signal in the fields of a packet communication system represented by Internet communication and mobile communication system, etc. Background Art When a speech signal is transmitted in a packet communication system represented by Internet communication or mobile communication system, a compression/coding technology is often used to enhance transmission efficiency of the speech signal. Many speech coding systems have been developed so far, and many lowbit rate speech coding systems developed in recent years separate a speech signal into a spectral envelope information and a sound source information andi compress/code the separated information pieces. For example, a CELP system described in Document 1 (M.R.Schroeder, B.S.Atal, "Code Excited Linear. Prediction: High Quality Speech at Low Bit. Rate" IEEE proc, ICASSP'85 pp.937-940) is one of its examples. Here, an overview of a CELP-based speech coder will be explainedusing FIG.l. Suppose an input speech signal is input to a speech coder successively every processing frame delimited by a time interval of approximately 20 ms. The input speech signal input to the speech coder for every processing frame is supplied to an LPC analysis section 11 first. The LPC analysis section 11 carries out an LPC (Linear Predictive Coding) analysis on the input speech signal, obtains an LPC vector having LPC coefficients as vector components, vector-quantizes the LPC vector obtained to obtain an LPC code, and decodes this LPC code to obtain a decoded LPC vector having decoded LPC coefficients as vector components. An excitation vector generation section 14 reads an adaptive codevector and fixed codevector from an adaptive codebook 12 and a fixed codebook 13 respectively and sends those codevectors to an LPC synthesis filter 15. The LPC synthesis filter 15 performs synthesis filtering on the adaptive codevector and the fixed codevector supplied from the excitation vector generation section 14 using an all-pole model synthesis filter having the decoded LPC coefficients given from the LPC analysis section 11 as filter coefficients and obtains a synthesized adaptive codevector and a synthesized fixed codevector, respectively. A comparison section 16 analyzes a relationship between the synthesized adaptive codevector, the synthesized fixed codevector output from the LPC synthesis filter 15 and the input speech signal, and calculates an adaptive codebook optimum gain to be multiplied on the synthesized adaptive codevector and a fixed codebook optimum gain to be multiplied on the synthesized fixed codevector, respectively. Furthermore, the comparison section 16 adds up the vector obtained by multiplying the synthesized adaptive codevector by the adaptive codebook optimum gain and the vector obtained by multiplying the synthesized fixed codevector by the fixed codebook optimum gain to obtain a synthesized speech vector and calculates a distortion between the synthesized speech vector obtained and input speech signal. The comparison section 16 further calculates distortions between many synthesized speech vectors obtained by operating the excitation vector generation section 14 and LPC synthesis filter 15 on all possible combinations of adaptive codevectors stored in the adaptive codebook 12 and fixed codevectors stored in the fixed codebook 13, and the input speech signal, determines an index of an adaptive codevector and an index of a fixed codevector that minimize the distortions from among those codevectors and sends the indices of the codevectors output from the respective codebooks, codevectors corresponding to the indices and an adaptive codebook optimum gain and fixed codebook optimum gain corresponding to the indices to a parameter coding section The parameter coding section 17 codes the adaptive codebook optimum gain and fixed codebook optimum gain to obtain gain codes, and outputs the gain codes obtained, the LPC code given from the LPC analysis section 11 and the indices of the respective codebooks together for each processing frame. The parameter coding section 17 further adds up two vectors; a vector obtained by multiplying the adaptive codevector corresponding to the index of the adaptive codebook by an adaptive codebook gain corresponding to the gain code and a vector obtained by multiplying the fixed codevector corresponding to the index of the fixed codebook by a fixed codebook gain corresponding to the gain code, thereby obtains an excitation vector and updates the old adaptive codevector in the adaptive codebook 12 with the excitation vector obtained. For synthesis filtering by the LPC synthesis filter 15, it is a general practice that linear predictive coefficients, high-pass filter and perceptual weighting filter using a long-term predictive coefficient obtained by carrying out a long-term predictive analysis on the input speech are used together. It is also a general practice that a search for optimum indices of the adaptive codebook and fixed codebook, calculation of optimum gains and coding processing of optimum gains are carried out in units of a subframe obtained by subdividing a frame. Next, an overview of processing of "vector quantization of LPC vector" carried out by the LPC analysis section 11 will be explained in more detail using FIG.2. Suppose that an LPC codebook 22 stores a plural entries of typical LPC vectors acquired beforehand by applying the LBG algorithm to many LPC vectors obtained by actually carrying out an LPC analysis on input speech signals of many processing frames . Withregardto theLBG algorithm, the details of its technology are disclosed in Document 2 (Y. Linde, A. Buzo, R. M. Gray, "An Algorithm for Vector Quantizer Design," IEEE trans. Comm., Vol. COM-28, No. 1, pp84-95, Jan., 1980). A quantization target vector input to the vector quantizer in FIG.2 (an LPC vector obtained by carrying out an LPC analysis on a speech signal in a processing frame section corresponds to the quantization target) is supplied to a distortion calculation section 21. Next, the distortion calculation section 21 calculates a Euclidean distortion between an LPC codevector stored in the LPC codebook 22 and the quantization target vector according to the following Expression (1): where in Expression (1) , XT is a quantization target vector, Cm is an mth (l