DESCRIPTION STOCHASTIC CODEBOOK EXCITATION VECTOR CODING METHOD Technical Field The present invention relates to a stochastic codebook excitation vector coding method in a CELP speech coding apparatus/speech decoding apparatus. Background Art When speech signals are transmitted in a packet communication system typified by Internet communication, a mobile communication system, or the like, compression and coding techniques are used to improve the speech signal transmission efficiency. Many speech coding methods have been developed to date, and many low bit rate speech coding methods developed in recent years, such as CELP, separate a speech signal into spectrum envelope information and spectrum detailed structure information, and perform compression and coding of the separated information. In a CELP speech coding apparatus, synthetic speech vectors are calculated for all combinations of adaptive code vectors stored by an adaptive codebook and fixed code vectors stored by a stochastic codebook, distance calculation is performed for each synthetic speech and input speech signal, and the adaptive code vector index and fixed code vector index for which the distance is smallest are found. One known stochastic codebook is an algebraic codebook. This codebook enables a stochastic codebook search to be performed with a comparatively small amount of calculation, and has consequently been widely used in CELP in recent years. An excitation vector of an algebraic codebook is composed of a small number of pulses with an amplitude of 1 and polarities ( + , -) , and the pulses (in this case, excitation vector waveform candidates) are positioned so as not to overlap each other. For example, when the subframe length is 32 and the number of pulses (= number of channels) is 4, the number of pulses per channel is 32/4 = 8, and the channel 0 pulse positions ici0[i0], channel 1 pulse positions icil[il], channel 2 pulse positions ici2[i2], and channel 3 pulse positions ici3[i3] are as shown below. Here, i0, i1, i2, and i3 denote indexes of the respective channels. ici0[i0] = {0, 4, 8, 12, 16, 20, 24, 28} ici1[i1] = {1, 5, 9, 13, 17, 21, 25, 29} ici2[i2] = {2, 6, 10, .14, 18, 22, 26, 30} ici3[i3] = {3, 7, 11, 15, 19, 23, 27, 31} A conventional stochastic codebook codes the pulse positions of each channel independently, and takes codes combining these with polarity codes as stochastic excitation vector codes. For example, in the above case of a subframe length of 32 and 4 channels, a conventional codebook 103 represents a pulse position of each channel as 3 bits, and together with the polarity code, performs coding using a code of (3+l)x4=16 bits. However, a problem with the above conventional stochastic codebook coding method is that, if the bit rate is low the bits assigned to each channel are also limited, and there are positions where there is no pulse at all, so that variations of an excitation vector waveform corresponding to a code (position information) decrease, and sound quality degradation occurs. In the above case of a subframe length of 32 and 4 channels, for example, there are positions where there is no pulse at all if coding is performed with fewer than 16 bits. Disclosure of Invention It is an object of the present invention to provide a stochastic codebook excitation vector coding method that enables variations to be secured so that there are no positions where there is no pulse at all while achieving a reduction of the number of bits used when coding stochastic codebook pulses. This object is achieved by associating a pulse position of a predetermined channel with a pulse position of another channel, searching for a pulse position by means of a predetermined algorithm, and taking a found pulse position code and a polarity code as a stochastic excitation vector code. Brief Description of Drawings FIG.1 is a block diagram showing the configuration of a CELP speech coding apparatus; FIG.2 is a flowchart showing an example of a pulse search algorithm for each channel in a coding method according to Embodiment 1 of the present invention; FIG. 3 is a flowchart showing an example of a pulse search algorithm for each channel in a coding method according to Embodiment 1 of the present invention; FIG. 4 is a flowchart showing an example of a pulse search algorithm for each channel in a coding method according to Embodiment 2 of the present invention; and FIG. 5 is a flowchart showing an example of a pulse search algorithm for each channel in a coding method according to Embodiment 2 of the present invention. Best Mode for Carrying out the Invention FIG.l is a block diagram showing the configuration of a CELP speech coding apparatus. An input speech signal is input sequentially to the speech coding apparatus divided into processing frames at time intervals of approximately 20 ms. The input speech signal input to the speech coding apparatus every processing frame is first supplied to an LPC analysis section 101. LPC analysis section 101 performs LPC (Linear Predictive Coding) of the input speech signal and obtains an LPC coefficient, performs vector quantization of the LPC coefficient to produce an LPC code, and decodes this LPC code to obtain a decoded LPC coefficient. An excitation vector creation section 104 reads an adaptive code vector and fixed code vector respectively from an adaptive codebook 102 and stochastic codebook 103, and sends these to an LPC combining section 105. LPC combining section 105 performs combining filtering of the adaptive code vector and fixed code vector supplied from excitation vector creation section 104, and the decoded LPC coefficient provided from LPC analysis section 101, with an all pole type combining filter in the filter coefficient, and obtains a combined adaptive code vector and combined fixed code vector. A comparison section 106 analyzes the relationship between the combined adaptive code vector and combined fixed code vector output from LPC combining section 105, and finds adaptive codebook optimum gain to be multiplied by the combined adaptive code vector, and stochastic codebook optimum gain to be multiplied by the combined fixed code vector. Comparison section 106 also adds together the vector obtained by multiplying the combined adaptive code vector by the adaptive codebook optimum gain and the vector obtained by multiplying the combined fixed code vector by the stochastic codebook optimum gain, and obtains a combined speech vector, and performs a distance calculation on the combined speech and input speech signal . Then comparison section 106 obtains the adaptive code vector stored by adaptive codebook 102 and the combined speech vector stored by stochastic codebook 103, and finds the adaptive code vector index and fixed code vector index for which the distance between the combined speech and input speech signal is smallest. Comparison section 106 then sends the indexes of the code vectors output from the codebooks, the code vectors corresponding to the respective indexes, and the adaptive codebook optimum gain and stochastic codebook optimum gain, to a parameter coding section 107. Parameter coding section 107 codes the adaptive codebook optimum gain and stochastic codebook optimum gain and obtains a gain code, and outputs the gain code, the LPC coefficient provided by LPC analysis section 101, and the indexes of each codebook together for each processing frame. Parameter coding section 107 also adds together the two vectors comprising the vector obtained by multiplying the adaptive code vector corresponding to the adaptive codebook index by the adaptive codebook gain corresponding to the gain code, and the vector obtained by multiplying the fixed code vector corresponding to the stochastic codebook index by the stochastic codebook gain corresponding to the gain code, and obtains a drive excitation vector, and updates the old adaptive code vector in adaptive codebook 102 with the drive excitation vector. Combining filtering by LPC combining section 105 generally makes combined use of a linear predictive coefficient, a high emphasis filter, and a weighting filter that uses a long-term predictive coefficient obtained by long-term predictive analysis of input speech. Adaptive codebook and stochastic codebook optimum index searches, optimum gain calculation, and optimum gain coding processing are generally carried out in sub frame units resulting from further division of a frame. In a speech decoding apparatus (decoder), the same configuration of LPC analysis section 101, adaptive codebook 102, stochastic codebook 103, excitation vector creation section 104, and LPC combining section 105 is provided as shown in FIG.1, and an excitation vector waveform is obtained by decoding codes transmitted from a speech coding apparatus . In order to reduce the amount of calculation, comparison section 106 usually searches for an adaptive codebook 102 excitation vector and stochastic codebook 103 excitation vector by means of an open-loop procedure. This open-loop search procedure is described below. (1) First, excitation vector creation section 104 chooses excitation vector candidates (adaptive excitation vectors) in succession from adaptive codebook 102 only, LPC combining section 105 creates a composite tone, and comparison section 106 carries out a comparison of the input speech and composite tone and selects the optimum adaptive codebook 102 code. At this time, gain is selected on the assumption that it is the value at which coding distortion is minimal (optimum gain). (2) Next, the above-described adaptive codebook code is fixed, excitation vector creation section 104 successively selects the same excitation vector from adaptive codebook 102 and stochastic codebook 103 successively selects the excitation vector (stochastic excitation vector) corresponding to the comparison section 106 code, LPC combining section 105 generates composite tones, and comparison section 106 compares the sum of both composite tones with the input speech and determines the optimum stochastic codebook 103 code. As in (1) above, gain is selected at this time on the assumption that it is the value at which coding distortion is minimal (optimum gain). Use of the above procedure to search for the optimum excitation vector results in a slight degradation of coding capability, but also a major reduction in the amount of calculation, compared with the method of searching for the optimum excitation vector by comparing combinations of all excitation vectors or both codebooks . The stochastic codebook 103 excitation vector search method will now be described in detail. Excitation vector code derivation is carried out by searching for the excitation vector that minimizes coding distortion E in Equation (1) below. In Equation (1) , x denotes the coding target; p, adaptive excitation vector gain; H, a weighting combining filter; a, an adaptive excitation vector; q, stochastic excitation vector gain; and s, a stochastic excitation vector. As the adaptive excitation vector search is performed by means of an open-loop procedure, stochastic codebook 103 code derivation is performed by searching for the excitation vector that minimizes coding distortion E in Equations (2) below. In Equations (2), y denotes the stochastic excitation vector search target vector. Here, gain values p and q are determined after the excitation vector search, and by making gain p = gain q = 1, Equations (2) above can be written as Equations (3) below. Minimi zing this distortion expression is equivalent to maximizing function C in Equation (4) below. Therefore, in the case of a search for an excitation vector composed of a small number of pulses such as an algebraic codebook excitation vector, calculating yH and HH beforehand enables function C above to be found with a small amount of calculation. yH can be found by reversing the order of vector y and convoluting matrix H, and then reversing the order of the result, and HH can be found by multiplication of the matrices. Stochastic codebook 103 searches for and codes a stochastic excitation vector using the procedure described in (1) through (4) below. (1) First, as preliminary processing, vector yH and matrix HH are found. (2) Next, pulse polarities are determined from the polarities (+ -) of vector yH elements. Specifically, the polarity of the pulse at each position is matched to the value of that position in yH, and the polarity of the yH value is stored in another array. After the polarities of all positions have been stored in another array, yH values are all made absolute values and converted to positive values. HH values are also converted in accordance with these polarities by performing polarity multiplication. (3) Next, function C shown in Equation (4) is found by adding yH and HH values using an n-fold loop (where n is the number of channels), and the pulse positions of the channels at which this value is largest are found. (4 ) The found pulse position of each channel is coded, and a code combining this with a polarity code is taken as the stochastic excitation vector code. With reference now to the accompanying drawings, stochastic codebook excitation vector coding methods according to embodiments of the present invention will be explained in detail below. In the descriptions of these embodiments, an algebraic codebook is used for which the subframe length is 32 and the number of pulses (= number of channels) is 4. (Embodiment 1) In Embodiment 1, a case is described in which an index of a predetermined channel is changed in accordance with another channel. In this embodiment, channel 0 pulse positions ici0[i0], channel 1 pulse positions icil[jl], channel 2 pulse positions ici2 [j2], and channel 3 pulse positions ici3[j3] are as shown below. iciO[i0] = {0, 4, 8, 12, 16, 20, 24, 28} icil[j1] = {1, 5, 9, 13, 17, 21, 25, 29} ici2[j2] = {2, 6, 10, 14, 18, 22, 26, 30} ici3[j3] = {3, 7, 11, 15, 19, 23, 27, 31} Here, i0 (0