the novel features believed characteristic of the invention are set forth in the appended claims . the invention itself , however , as well as a preferred mode of use , further objectives and advantages thereof , will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings , wherein : fig1 shows a schematic block diagram of an example of a hardware configuration for carrying out the present invention . in fig1 , a sound generated near a microphone 102 is inputted as an analog signal to an analog - to - digital ( a / d ) converter 104 by the microphone 102 . thereafter , the a / d converter 104 converts the analog signal into a digital signal that can be processed by a cpu 106 . in the case of a car navigation system , for example , the sounds captured by the microphone 102 include not only speeches of a driver and a passenger but also an air blowing sound of an air conditioner , sounds outputted from a car audio system , an engine sound , a sound of a horn , and the like . in the case where a car window is opened , the sounds received by the microphone 102 also include noise from oncoming cars , voices of passers - by , and the like . the cpu 106 performs , on digitized speech signals thus received , processing required for speech information processing , such as discrete fourier transformation and discrete cosine transformation , by operating preinstalled software ( not shown ). specific processing performed by the cpu 106 will be described in detail later . the cpu 106 requires a certain size of memory region to perform the speech information processing described above . for example , since matrix calculation is executed for the discrete fourier transformation and the discrete cosine transformation , a region to store variables of a matrix and intermediate values is required . thus , it is preferable that a memory 108 that is a random access memory ( ram ) is connected to the cpu 106 so as to enable data to be read and written by the cpu 106 . a display device 110 is connected to the cpu 106 through a suitable input - output interface ( not shown ). the display device 110 is used to provide a user with processing contents and status , and other visual information . in the embodiment shown in fig1 , a digital speech signal obtained as a result of processing by the cpu 106 is converted into an analog signal by a digital - to - analog ( d / a ) converter 112 . this analog signal is sent to a speech recognition device and processed . moreover , in this case , since the signal is analog , the user can also listen to the signal as it is through a speaker ( not shown ). thus , the above configuration can also be used for a hearing aid . an embodiment shown in fig2 has the same configuration as that shown in fig1 , except that the d / a converter 112 in fig1 is replaced by a communication device 202 . in the embodiment shown in fig2 , a digital speech signal obtained as a result of processing by a cpu 106 is sent to a speech recognition device using a digital communication method such as pci and ethernet ( trademark ), and processed . an embodiment shown in fig3 has the same configuration as that shown in fig1 , except that the d / a converter 112 in fig1 is replaced by a shared memory 302 . the shared memory 302 may be a part of a memory 108 or may be a separate memory . in the embodiment shown in fig3 , a digital speech signal obtained as a result of processing by a cpu 106 is stored in the shared memory 302 and data of the speech signal is read from the shared memory by a speech recognition device at suitable timing . fig4 shows preprocessing for typical speech recognition which has heretofore been known . a speech signal that enters from the left side of fig4 is assumed to be previously converted into a digital signal by the a / d converter 104 shown in each of fig1 to 3 . in a block 402 , frame processing is first performed on the digital speech signal using an appropriate method , such as a hanning windowing and a hamming windowing , and then discrete fourier transformation is performed thereon . next , in a block 404 , mel - filter discrete cosine transformation is performed on a spectrum of the speech signal to obtain a mel - cepstrum that is a cepstrum on a log scale . here , the reason why the log scale is used is that human hearing characteristics is empirically shown to be based on the log scale . thereafter , in a block 406 , the mel - cepstrum thus obtained is used to perform speech recognition by using a known method such as hidden markov model ( hmm ), an acoustic model and an n - gram language model . however , since speech recognition processing itself is not the subject matter of the present invention , further description thereof will be omitted . fig5 shows positioning of a technique of the present invention . specifically , as shown in fig5 , a processing block 502 of the present invention can be inserted between the block 402 for outputting the spectrum of the speech signal and the block 404 for outputting the mel - cepstrum in the conventional technique , while maintaining consistency of the entire processing . in other words , the processing block 502 of the present invention can be inserted between the blocks in the conventional technique without modifying functions of the blocks of the conventional technique . this is one of the advantageous features of the processing technique of the present invention . the functions of the processing block 502 are summarized as follows . specifically , a filter is prepared directly from an observed speech itself and an output filtered with the filter is sent to the block 404 . this filter preparation processing is performed independently for each speech frame . in other words , a new filter is designed each for the respective speech frame . incidentally the acoustic model used by a speech recognition part in the block 406 is trained by using a speech also in that training , it is preferable to use the block 502 of the present invention . with reference to fig6 , speech processing according to an embodiment of the present invention will be described . fig6 shows an example of a spectrum 602 of an observed speech . assuming that j is a bin number of discrete fourier transformation , and that y t ( j ) is a power spectrum of a t - th frame of the observed speech , a log power spectrum y t ( j ) is calculated by the following equation in step 604 : moreover , in calculation of the log power spectrum of a t - th frame , an average of power spectra of several frames before and after the t - th frame may be used to obtain a more stable expression . thus , the term “ log power spectrum ” in the present specification also includes a log power spectrum of the average of the power spectra of several frames before and after the target frame . note that the bin number corresponds to a frequency of the discrete fourier transformation . for example , when 512 points of discrete fourier transformation is performed at a sampling frequency of 16 khz , the following result is obtained : specifically , outputs of the discrete fourier transformation are put together for each stepwise frequency and referred to by the number . a log power spectrum 604 a is obtained by performing the above - described processing on a noise example . a log power spectrum 604 b is obtained by performing the above - described processing on a speech example . as shown in fig6 , the noise example and the speech example are taken from different frames . next , assuming that a discrete cosine transformation matrix is d ( i , j ), a cepstrum c t ( i ) is obtained as follows in step 606 : a cepstrum 606 a is obtained by performing the above - described processing on the noise example . a cepstrum 606 b is obtained by performing the above - described processing on the speech example . the cepstra represent the curvatures of the log power spectra . the lower cepstra correspond to long oscillations , and the upper cepstra correspond to short oscillations . we need only the medium oscillations so to cover possible harmonic structures in the human voice . thus , in step 608 , the lower and the upper cepstra should be filtered out . specifically , processing expressed by the following formula is performed : a compensated cepstrum 608 a is obtained by performing the above - described processing on the noise example . a compensated cepstrum 608 b is obtained by performing the above - described processing on the speech example . in the above formula , ε is 0 or a very small constant . moreover , lower_cep_num and upper_cep_num correspond to a possible range as the harmonic structure of the human speech . in one experimental example , assuming that a fundamental frequency of the human speech is between 100 mhz and 400 mhz , lower_cep_num is set to 40 and upper_cep_num is set to 160 . note , however , that this is a setting example in the case where the sampling frequency is 16 khz and a fast fourier transformation ( fft ) length is 512 points . next , in step 610 , inverse discrete cosine transformation is performed on the cepstrum processed in step 608 , thereby bringing the processed cepstrum back to a log power spectrum domain . specifically , processing expressed by the following formula is performed : here , d − 1 ( j , i ) represents i and j components of an inverse discrete cosine transformation matrix d − 1 . d − 1 denotes an inverse matrix of the discrete cosine transformation matrix d described above . generally , since d is a unitary matrix , d − 1 is obtained as a transposed matrix of d . a compensated log power spectrum 610 a is obtained by performing the above - described processing on the noise example . a compensated log power spectrum 610 b is obtained by performing the above - described processing on the speech example . in step 612 , first , performed is processing of bringing the result obtained by performing the inverse discrete cosine transformation back to a power spectrum domain from the log power spectrum domain using the following formula . specifically , the processing expressed by the following formula is performed : next , normalization is performed on the result of step 612 by using the following formula so that its average value can be 1 , thereby finally preparing a filter . in the following formula , num_bin denotes the total number of bin . a filter 612 a shows such a filter obtained from the noise example . a filter 612 b shows such a filter obtained from the speech example . as shown in the filters 612 a and 612 b , the filter of the noise example has low and gentle peaks and the filter of the speech example has high and sharp peaks in general : in step 614 , as expressed by the following formula , the power spectrum of the frame t is multiplied by the signal thus obtained as a weighting factor for each bin . thus , the power spectrum is filtered to enhance the observed speech : z t ( j )= y t ( j ) w t ( j ) [ formula 7 ] z t ( j ) thus obtained is a power spectrum of an output speech in the t - th frame of the bin number j . a spectrum 614 a is obtained from the noise example and a spectrum 614 b is obtained from the speech example . here , comparison of the spectrum 604 a and the spectrum 614 a shows that the spectrum of the noise example remains substantially unchanged by the processing shown in fig6 . on the other hand , in the case of the speech example , spectrum 614 b obtained as a result of the processing shown in fig6 has higher formants than the spectrum 604 b . this shows that speech recognition can be performed with high accuracy even under noisy environment . meanwhile , application of the present invention adds another discrete cosine transformation and another inverse discrete cosine transformation to the case of no application of the present invention . in other words , a calculation of multiplying a 256 - dimensional vector by a 256 × 256 matrix is performed twice when the fft length is 512 points . although this calculation amount is not so considerable as that required for speech recognition itself , the calculation amount increased by the application of the present invention can be reduced by reducing executions of the respective transformations from twice to once . y . . . vector of log power spectrum of observed speech 256 dimensions d . . . discrete cosine transformation matrix 256 × 256 dimensions a . . . matrix for cutting off upper and lower terms 256 × 256 dimensions w . . . log power spectrum after inverse discrete cosine transformation 256 dimensions in this matrix , ε is the same small number as that described in step 608 of fig6 . note that a is a matrix having the following format : according to the above description , w = d − 1 ady . here , since both of d and a are matrices of fixed values , d − 1 ad can be calculated to obtain one matrix , in advance . thereby the calculation amount can be reduced , in other words , calculation time can be shortened . fig7 shows a functional block diagram in the case where a speech - enhanced output is provided as a time - domain speech signal to a speech recognition device . for example , individual functional blocks shown in fig7 can also be regarded as individual software processing modules in a speech processing system according to the present invention . in fig7 , a speech input part 702 provides a digital speech signal using the function of the a / d converter 104 shown in fig1 . a discrete fourier transformation ( dft ) part 704 outputs a power spectrum using a discrete fourier transformation function . a log power spectrum generation part 706 receives the power spectrum from the dft part 704 and executes the function described as the processing step 604 in fig6 . a dct part 708 receives an output from the log power spectrum generation part 706 and executes the function described as the processing step 606 in fig6 . a cepstrum upper and lower term cut - off part 710 receives an output from the dct part 708 and executes the function described as the processing step 608 in fig6 . an i - dct part 712 receives an output from the cepstrum upper and lower term cut - off part 710 and executes the function described as the processing step 610 in fig6 . a filter preparation part 714 receives an output from the i - dct part 712 and executes the function described as the processing step 612 in fig6 . a filtering execution part 716 receives the power spectrum from the dft part 704 and an output from the filter preparation part 714 and executes the function described as the processing step 614 in fig6 . an output from the filtering execution part 716 and an output from the speech input part 702 are inputted to an inverse discrete fourier transformation ( i - dft ) part 718 and converted into a time - domain digital speech signal . this time - domain digital speech signal is outputted as a speech signal that is audible to humans , by an output part having a d / a converter and a speaker , for example . fig8 shows a functional block diagram in the case where a speech - enhanced output is provided as a spectral - domain speech signal to a speech recognition device . similarly , here , individual functional blocks shown in fig8 can also be regarded as individual software processing modules in a speech processing system according to the present invention . in fig8 , a speech input part 802 provides a digital speech signal using the function of the a / d converter 104 shown in fig1 . a dft part 804 outputs a power spectrum using a discrete fourier transformation function . a log power spectrum generation part 806 receives the power spectrum from the dft part 804 and executes the function described as the processing step 604 in fig6 . a dct part 808 receives an output from the log power spectrum generation part 806 and executes the function described as the processing step 606 in fig6 . a cepstrum upper term and lower term cut - off part 810 receives an output from the dct part 808 and executes the function described as the processing step 608 in fig6 . an i - dct part 812 receives an output from the cepstrum upper term and lower term cut - off part 810 and executes the function described as the processing step 610 in fig6 . a filter preparation part 814 receives an output from the i - dct part 812 and executes the function described as the processing step 612 in fig6 . a filtering execution part 816 receives the power spectrum from the dft part 804 and an output from the filter preparation part 814 and executes the function described as the processing step 614 in fig6 . an output from the filtering execution part 816 is provided as a spectral - domain speech signal to the speech recognition device . an output part 818 may be either the communication device 202 shown in fig2 or the shared memory 302 shown in fig3 . next , description will be given of an evaluation experiment on speech processing according to the present invention . for the evaluation experiment , in - vehicle speech recognition evaluation database censrec - 3 ( corpus and environments for noisy speech recognition ) is used , which is created by information processing society of japan ( ipsj ) sig - slp noisy speech recognition evaluation working group . censrec - 3 includes spoken words uttered inside a vehicle under various environmental conditions such as normal running , hazard indicator on , air conditioner on ( low ), air conditioner on ( high ), audio system on and opened window . speech data are classified into two categories : one for training and the other for evaluation . an acoustic model is prepared by using the data for training and the acoustic model is used to execute speech recognition on the data for evaluation . finally , recognition rates are evaluated . here , normal speech recognition execution without changing a speech recognition front end or a result thereof is called a base line . accordingly , evaluation is made on how much the recognition rates are improved from the base line by changing the speech recognition front end as in the case of the present invention . censrec - 3 includes evaluation conditions 1 to 6 determined by which to select as the data for training and evaluation . the evaluation experiment is carried out this time under the condition 3 close to a practical scenario , in which speech data collected by a remote microphone are used for both training and evaluation . the following table shows results of word recognition rates ( accuracy %). in this evaluation experiment , since a drawback is expected with present invention when the background noise contains music or speech from audio devices , the results are divided into those in the case of audio system on and those in all the other cases . moreover , the results in the cases of opened window and air conditioner on ( high ), in which improvement can be expected , are also individually shown in the table . in the speech recognition subjected to the processing of the present invention , the recognition rates are improved from the base line in all the cases but the case of audio system on . particularly , significant improvement is achieved in the cases of opened window and air conditioner on ( high ), which are target environmental conditions of the present invention . on the other hand , in the case of audio system on , the recognition rate is deteriorated compared with the base line . note , however , sounds from the audio system can be effectively reduced by using a well - known echo canceling technique . thus , it should be understood that the degradation caused by the audio system can be relatively easily reduced . the technique of the present invention can be used simultaneously with a heretofore known noise reduction technique such as spectral subtraction ( ss ). as is clear from the results of “ ss + present invention ” in the table , a combination of ss and the technique of the present invention achieves significant improvement in the recognition rate , compared with the case of using only ss or the case of using only the technique of the present invention , in all the cases except the case of audio system on . according to the present invention , a filter is designed directly from an observed speech itself . thus , this designing method of filters makes it possible to obtain a filter that gives weight to a harmonic structure portion in a speech sound segment and a filter that is approximately flat in a non - speech sound segment having no harmonic structure . since these differences of filers are continuous , processing can be stably performed without making any distinction between the speech sound segment and the non - speech sound segment . in the foregoing embodiments of the present invention , the present invention has been described above mainly as the preprocessing system of the speech recognition system . however , the present invention is not limited to such application but can be suitably applied to other uses , such as a hearing aid , by utilizing its noise reduction characteristics and employing the configuration as shown in fig1 . while the present invention has been described with reference to what are presently considered to be the preferred embodiments , it is to be understood that the invention is not limited to the disclosed embodiments . the present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims . the scope of the following claims is to be accorded the broadcast interpretation so as to encompass all such modifications and equivalent structures and functions .