Abstract:
A dividing module divides a voice signal into voice frames. A likelihood value generation module compares each of the voice frames with a first voice model and a second voice model to generate first likelihood values and second likelihood values. A decision module decides a windows size according to the first likelihood values and the second likelihood values. An accumulation module accumulates the first likelihood values and the second likelihood values inside the window size to generate a first sum and a second sum. A determination module determines whether the voice signal is abnormal according to the first sum and the second sum. While the voice has a big change in the environment, the decision module can dynamically adapt the windows size for decreasing the false rate of the detection and speeding up the determining of the abnormal voice.

Description:
[0001]    This application claims priority to Taiwan Patent Application No. 095144391 filed on Nov. 30, 2006. 
       CROSS-REFERENCES TO RELATED APPLICATIONS 
       [0002]    Not applicable. 
       BACKGROUND OF THE INVENTION 
       [0003]    1. Field of the Invention 
         [0004]    The present invention relates to a voice detection apparatus, a method, and a computer readable medium thereof. More specifically, it relates to a voice detection apparatus, a method, and a computer readable medium capable of deciding a window size dynamically 
         [0005]    2. Descriptions of the Related Art 
         [0006]    With the development of voice detection techniques in recent years, various voice detection applications are produced. In general voice detection, detected voices can be classified into two major types: a normal voice and an abnormal voice. The normal voice is the voice that is relatively not noticed in an environment, such as voices of a vehicle on a street, voices of people talking, and voices of broadcasting music, etc. The abnormal voice is the voice that is noticed, such as voices of screaming, voices of crying and voices of calling for help, etc. Especially for the aspects of security assurance and surveillance, the voice detection can help security service personnel to handle emergency. 
         [0007]    A Gaussion Mixture Model (GMM) is frequently used for voice recognition or speaker recognition in recent years. The GMM is an extension of a MonoGaussian Model (MGM) which uses a mean vector to record the center positions of a number of samples in a vector space and performs an approximate calculation on the shapes of these samples distributed in the vector space with a covariance matrix. Except that the GMM has a characteristic of the MGM, the model also combines a characteristic of a Vector Quantization (VQ) which is capable of recording some material positions of various types of the samples in the vector space. 
         [0008]      FIG. 1  shows a conventional voice detection apparatus  1  which comprises a receiving module  100 , a division module  101 , a characteristic retrieval module  102 , a comparison module  103 , an accumulation module  104  and a determination module  105 . The voice detection apparatus  1  is connected to a database  106 , wherein the database  106  stores a plurality of voice models that are all the GMM and can be classified into two types: a normal voice model and an abnormal voice model. The receiving module  100  is used to receive a voice signal  107  and the division module  101  divides the voice signal  107  into a plurality of voice frames, wherein two adjacent voice frames might overlap. Then, the characteristic retrieval module  102  retrieves characteristic parameters of each voice frame. The comparison module  103  performs a likelihood comparison on the characteristic parameters of each voice frames based on the normal and abnormal voice models pre-stored in the database  106  to generate a plurality of first likelihood values and a plurality of second likelihood values respectively. The accumulation module  104  accumulates the first likelihood values and the second likelihood values respectively according to a window size, wherein the window size corresponds to a fixed period of time. As shown in  FIG. 2 , the voice signal  107  can be divided into a plurality of areas such as areas  21 ,  22 ,  23 ,  24  and  25 . The size of each area is the window size. Each area comprises many voice frames. Assuming that the window size is 400 ms, the size of the voice frame is 10 ms, and an overlapped portion between two voice frames is 0 ms, then each area comprises 40 voice frames. The accumulation module  104  accumulates all the first likelihood values and the second likelihood values of the 40 voice frames of each area to generate a first sum and a second sum, respectively. The determination module  105  determines whether the voice signal  107  is normal or abnormal according to the first sum and the second sum. 
         [0009]    However, since the window size of the conventional voice detection apparatus  1  is fixed, a false possibility of detection will increase substantially while the environment voice or background voice of a voice signal has a significant change. Under such circumstances, the conventional voice detection apparatus  1  fails to respond immediately and correctly because the change of the environment voice would be treated as abnormal voices. Consequently, how to dynamically adjust the window size to enhance the overall performance of the voice detection apparatus is a serious problem in the industry. 
       SUMMARY OF THE INVENTION 
       [0010]    One objective of this invention is to provide a voice detection apparatus comprising a receiving module, a division module, a likelihood value generation module, a decision module, an accumulation module and a determination module. The receiving module is used to receive a voice signal. The division module is used to divide the voice signal into a plurality of voice frames. The likelihood value generation module is used to compare each of the voice frames with a first voice model and a second voice model to generate a plurality of first likelihood values and second likelihood values. The decision module is used to decide a window size according to the first likelihood values and the second likelihood values. The accumulation module is used to accumulate the first likelihood values and the second likelihood values inside the window size to generate a first sum and a second sum. The determination module is used to determine whether the voice signal is abnormal according to the first sum and the second sum. 
         [0011]    Another objective of this invention is to provide a voice detection method comprising the following steps: receiving a voice signal; dividing the voice signal into a plurality of voice frames; comparing each of the voice frames with a first voice model and a second voice model to generate a plurality of first likelihood values and second likelihood values; deciding a window size according to the first likelihood values and the second likelihood values; accumulating the first likelihood values and the second likelihood values inside the window size to generate a first sum and a second sum; and determining whether the voice signal is abnormal according to the first sum and the second sum. 
         [0012]    Yet a further objective of the invention is to provide a computer readable medium storing an application program that has code to make a voice detection apparatus execute the above-mentioned voice detection method. 
         [0013]    While the environment voice or background voice of a voice signal has a significant change, the invention can dynamically adjust the window size for decreasing the false possibility of the detection so that the response is instant and correct. Especially for the security assurance applications, the invention can detect an abnormal voice more precisely so a real-time response can be transmitted to a security service office in time. 
         [0014]    The detailed technology and preferred embodiments implemented for the subject invention are described in the following paragraphs accompanying the appended drawings for people skilled in this field to well appreciate the features of the claimed invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]      FIG. 1  is a schematic diagram of a conventional voice detection apparatus; 
           [0016]      FIG. 2  is a schematic diagram of a conventional decision window; 
           [0017]      FIG. 3  is a schematic diagram of a first embodiment of the invention; 
           [0018]      FIG. 4  is a schematic diagram of a likelihood value generation module of the first embodiment; 
           [0019]      FIG. 5  is a schematic diagram of a decision module of the first embodiment; 
           [0020]      FIG. 6  is a schematic diagram of a decision window of the invention; 
           [0021]      FIG. 7  is a coordinate diagram to show how to calculate a window size of the invention; 
           [0022]      FIG. 8  is a flow chart of a second embodiment of the invention; 
           [0023]      FIG. 9  is a flow chart of step  802  of the second embodiment; 
           [0024]      FIG. 10  is a flow chart of step  803  of the second embodiment; 
           [0025]      FIG. 11  is a flow chart of a third embodiment of the invention; 
           [0026]      FIG. 12  is a flow chart of step  1102  of the third embodiment; and 
           [0027]      FIG. 13  is a flow chart of step  1103  of the third embodiment. 
       
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0028]    A first embodiment of the invention is shown in  FIG. 3  which is a voice detection apparatus  3  that comprises a receiving module  300 , a division module  302 , a likelihood value generation module  303 , a decision module  305 , an accumulation module  306  and a determination module  307 . The apparatus  3  is connected to a database  304  that stores a plurality of voice models. The voice models are all a Gaussion Mixture Model (GMM) and can be classified into normal voice models and abnormal voice models. The receiving module  300  is used to receive a voice signal  301 . The division module  302  is used to divide the voice signal  301  into a plurality of voice frames  309  by utilizing a conventional technique. Two adjacent voice frames of the voice frames  309  might overlap. The voice frames  309  is transmitted to the likelihood value generation module  303  to generate a plurality of first likelihood values  310  and a plurality of second likelihood values  311 .  FIG. 4  is a schematic diagram of the likelihood value generation module  303 . The likelihood value generation module  303  comprises a characteristic retrieval module  400  and a comparison module  401 . The characteristic retrieval module  400  retrieves at least one characteristic parameter  402  from each of the voice frames  309 . The characteristic parameter  402  can be one of a Mel-scale Frequency Cepstral Coefficient (MFCC), a Linear Predictive Cepstral Coefficient (LPCC), and a cepstral of the voice signal  301 , or a combination thereof. The comparison module  401  performs the likelihood comparison on the characteristic parameter  402  with the normal and abnormal voice models  308  pre-stored in the database  304  to generate the first likelihood values  310  and the second likelihood values  311 . More particularly, a whole Gaussian mixture density function mainly consists of M component densities, wherein each of the M component densities can be defined by three parameters: a mean vector, a covariance matrix and a mixture weight. In the invention, both a normal voice (the background voice) and an abnormal voice have a corresponding GMM model A which is a set of all the parameters as shown in the following equation: 
         [0000]      λ={w i ,u i ,Σ i }, i=1 . . . M 
         [0000]    wherein w i  represents the mixture weight, μ i  represents the mean vector, Σ i  represents the covariance matrix, and M represents the number of a Gaussian distribution. The Gaussian mixture density is a weighted sum of M component densities (i.e., λ) as shown in the following equation: 
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         [0000]    wherein x is a random vector in D dimensions or a characteristic vector of one voice frame in D dimensions, b i (x), i=1, . . . , M is component densities, w i , i=1, . . . , M is mixture weights satisfying a limitation that a summation of all M mixture weights should be 1, i.e., 
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         [0029]    Each of the component densities b i (x), i=1, . . . , M is the D dimensional Gaussian density function as shown in the following equation: 
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         [0000]    wherein μ i  is the mean vector and Σ i  is the covariance matrix. 
         [0030]    Assuming that λ 1  and λ 2  respectively represent a GMM model for a normal voice and a GMM model for an abnormal voice, and x i  represents a sequence of voice frames, a plurality of likelihood values A and a plurality of likelihood values B are generated after performing the likelihood calculation on each of the voice frames based on λ 1  and λ 2 , i.e., based on the equation 
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         [0000]    After performing a logarithm operation on the likelihood values A and B, a plurality of likelihood log values C and a plurality of likelihood log values D are obtained. The likelihood log values C and D are the first likelihood values  310  and the second likelihood values  311 , wherein the first likelihood values  310  are the results of performing the likelihood comparison on the normal voice model and the characteristic parameter  402 , and the second likelihood values  311  are the results of performing the likelihood comparison on the abnormal voice model and the characteristic parameter  402 . Both of the results are transmitted to the decision module  305 . 
         [0031]      FIG. 5  shows a schematic diagram of the decision module  305 . The decision module  305  is used to decide a window size. The decision module  305  comprises a first calculation module  500  and a second calculation module  501 . The first calculation module  500  accumulates the first likelihood values  310  and second likelihood values  311  respectively based on a predetermined minimum window in order to generate a minimum window likelihood differential value  502 . More particularly, as shown in  FIG. 6 , assume that the voice signal  301  has a length of 10 seconds, and the size of the voice frame and the size of a minimum window  600  are 5 ms and 100 ms, respectively. The first calculation module  500  accumulates the 20 first likelihood values  310  and the 20 second likelihood values  311  that locate from the beginning to 100 ms. The first calculation module  500  takes the difference of the accumulation results of the first likelihood values  310  and the second likelihood values  311 . The minimum window likelihood differential value  502  is the difference. 
         [0032]      FIG. 7  shows how to derive the window size  312  with the second calculation module  501 , wherein the N in the x axis represents minimum window likelihood differential values, and the y axis represents the parameter value. The invention defines a first minimum window likelihood difference constant N 1  and a second minimum window likelihood difference constant N 2 . In this embodiment, N 1  and N 2  are 300 and 600, respectively, and stored in the second calculation module  501 . Both of N 1  and N 2  can be other constants according to the practical conditions so the values of N 1  and N 2  are not used to limit the scope of this invention.  FIG. 7  further shows a first weighting linear equation M 1  and a second weighting linear equation M 2 . The weighting linear equations are shown as follows: 
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         [0033]    Assuming that the N derived by the first calculation module  500  equals to 480, the second calculation module  401  utilizes the aforementioned first weighting linear equation M 1  and the second weighting linear equation M 2  to derive that M 1 (N) is 0.4 and M 2 (N) is 0.6. 
         [0034]    Furthermore, the number of the voice frames N can be substituted into the following linear equation to derive parameters f 1 (N) and f 2 (N): 
         [0000]        f   1 ( N )= a   1   ·N+b   1    
         [0000]        f   2 ( N )= a   2   ·N+b   2    
         [0000]    wherein a 1 , a 2 , b 1  and b 2  are predetermined constants, and the settings of a 1 , a 2 , b 1  and b 2  constants should make f 1 (N) larger and f 2 (N) smaller. In other words, f 1 (N) is a larger window value and f 2 (N) is a smaller window value. Then, the second calculation module  501  derives the window size  312  according to the following equation: 
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         [0035]    By utilizing the equation to derive the window size  312 , the window size value is relatively larger while the minimum window likelihood differential value N is a smaller value. On the contrary, the window size value is relatively smaller while the minimum window likelihood differential value N is a larger value. The window size  312  is the size of the decision window  601  in  FIG. 6 . 
         [0036]    Refer back to  FIG. 3 . After the window size  312  is obtained, the accumulation module  306  accumulates the first likelihood values and the second likelihood values of the voice frames inside the window size  312  to generate a first sum  313  and a second sum  314 , respectively. The determination module  307  determines whether the voice signal  301  is abnormal according to the first sum  313  and the second sum  314 . If the first sum  313  is greater, the voice signal  301  is determined normal. Otherwise, the voice signal  301  is determined abnormal. 
         [0037]    A second embodiment of the invention is shown in  FIG. 8  which is a flow chart of a voice detection method. In step  800 , a voice signal is received. Next, step  801  is executed for dividing the voice signal into a plurality of voice frames and two adjacent voice frames might overlap. Next, step  802  is executed for comparing each of the voice frames with the pre-stored normal and abnormal voice models to generate a plurality of first likelihood values and second likelihood values. More particularly, as shown in  FIG. 9 , step  802  further comprises step  900  and step  901 , wherein in step  900 , at least one characteristic parameter is retrieved from each of the voice frames. The characteristic parameter can be one of a Mel-scale Frequency Cepstral Coefficients (MFCC), a Linear Predictive Cepstral Coefficient (LPCC), and a cepstral of the voice signal, or a combination thereof. In step  901 , the pre-stored normal and abnormal voice models are taken out to perform the likelihood comparison with the characteristic parameter of each of the voice frames to generate the first likelihood values and the second likelihood values, respectively. More particularly, a whole Gaussian mixture density function is mainly consists of M component densities, wherein each of the M component densities can be defined by three parameters: a mean vector, a covariance matrix and a mixture weight. In the invention, both a normal voice (the background voice) and an abnormal voice have a corresponding GMM model λ which is a set of all the parameters as shown in the following equation: 
         [0000]      λ={w i ,u i ,Σ i }, i=1 . . . M 
         [0000]    wherein w i  represents the mixture weight, μ i  represents the mean vector, Σ i  represents the covariance matrix, and M represents the number of a Gaussian distribution. The Gaussian mixture density is a weighted sum of M component densities (i.e., λ) as shown in the following equation: 
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         [0000]    wherein x is a random vector in D dimensions or a characteristic vector of one voice frame in D dimensions, b i (x), i=1, . . . , M is component densities, w i , i=1, . . . , M is mixture weights satisfying a limitation that a summation of all M mixture weights should be 1, i.e., 
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         [0038]    Each of the component densities b i (x), i=1, . . . , M is the D dimensional Gaussian density function as shown in the following equation: 
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         [0000]    wherein μ i  is the mean vector and Σ i  is the covariance matrixe. 
         [0039]    Assuming that λ 1  and λ 2  respectively represents a GMM model for a normal voice and a GMM model for an abnormal voice, and x i  represents a sequence of voice frames, a plurality of likelihood values A and a plurality of likelihood values B are generated after performing the likelihood calculation on each of the voice frames based on λ 1  and λ 2 , i.e., based on the equation 
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         [0000]    After performing a logarithm operation on the likelihood A and B, a plurality of likelihood log values C and a plurality of likelihood log values D are obtained. The likelihood log values C and D are the first likelihood values  310  and the second likelihood values  311 , wherein the first likelihood values are the results of performing the likelihood comparison on the normal voice model and the characteristic parameter, and the second likelihood values are the results of performing the likelihood comparison on the abnormal voice model and the characteristic parameter. 
         [0040]    Next, step  803  is executed for deciding a window size. More particularly, as shown in  FIG. 10 , step  803  comprises step  1000  and step  1001 . In step  1000 , the first likelihood values and the second likelihood values are accumulated respectively based on a predetermined minimum window. More particularly, as shown in  FIG. 6 , the voice signal is a continuous signal with an assumed length of 10 seconds, and the size of the voice frame and the size of a minimum window  600  are 5 ms and 100 ms, respectively. The first calculation module  500  individually accumulates the 20 first likelihood values and the 20 second likelihood values that locate from the beginning to 100 ms and takes the difference of the accumulation results of the first likelihood values and the second likelihood values to generate the minimum window likelihood differential value. 
         [0041]      FIG. 7  shows how to derive the window size. A first weighting linear equation M 1  and a second weighting linear equation M 2  in  FIG. 7  are shown as follows: 
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                                 2 
                               
                               - 
                               N 
                             
                             
                               
                                 N 
                                 2 
                               
                               - 
                               
                                 N 
                                 1 
                               
                             
                           
                         
                       
                       
                         
                             
                            
                           
                             
                               N 
                               1 
                             
                             ≤ 
                             N 
                             ≤ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           0 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≥ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                   
                    
                   
                     
 
                   
                    
                   
                     
                       M 
                       2 
                     
                      
                     
                       ( 
                       N 
                       ) 
                     
                   
                 
                 = 
                 
                   { 
                   
                     
                       
                         
                             
                            
                           0 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≤ 
                             
                               N 
                               1 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           
                             
                               N 
                               - 
                               
                                 N 
                                 1 
                               
                             
                             
                               
                                 N 
                                 2 
                               
                               - 
                               
                                 N 
                                 1 
                               
                             
                           
                         
                       
                       
                         
                             
                            
                           
                             
                               N 
                               1 
                             
                             ≤ 
                             N 
                             ≤ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           1 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≥ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0042]    Assuming that the minimum window likelihood differential value N derived in step  1000  equals to 480, by utilizing the aforementioned first weighting linear equation M 1  and the second weighting linear equation M 2 , step  1001  is executed for deriving that M 1 (N) is 0.4 and M 2 (N) is 0.6. 
         [0043]    Furthermore, the number of the voice frames N can be substituted into the following linear equation to derive parameters f 1 (N) and f 2 (N): 
         [0000]        f   1 ( N )= a   1   ·N+b   1    
         [0000]        f   2 ( N )= a   2   ·N+b   2    
         [0000]    wherein a 1 , a 2 , b 1  and b 2  are predetermined constants, and the settings of a 1 , a 2 , b 1  and b 2  constants should make f 1 (N) larger and f 2 (N) smaller. In other words, f 1 (N) is a larger window value and f 2 (N) is a smaller window value. Then, step  1101  is executed for deriving the window size according to the following equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     window 
                      
                     
                         
                     
                      
                     size 
                   
                   = 
                   
                     
                       
                         
                           
                             M 
                             1 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                         · 
                         
                           
                             f 
                             1 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                       
                       + 
                       
                         
                           
                             M 
                             2 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                         · 
                         
                           
                             f 
                             2 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                       
                     
                     
                       
                         
                           M 
                           1 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                       + 
                       
                         
                           M 
                           2 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                   
                 
               
             
             
               
                 
                   = 
                   
                     
                       0.4 
                        
                       
                         
                           f 
                           1 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                     + 
                     
                       0.6 
                        
                       
                         
                           f 
                           2 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0044]    By utilizing the equation to derive the window size, the window size value is a relatively larger while the minimum window likelihood differential value N is a smaller value. On the contrary, the window size value is relatively smaller, while the minimum window likelihood differential value N is a larger value. The window size mentioned here is the size of the decision window  601  in  FIG. 6 . 
         [0045]    Refer back to  FIG. 8 . After the window size is obtained, step  804  is executed for accumulating the first likelihood values and the second likelihood values of the voice frames inside the window size to generate a first sum and a second sum, respectively. Step  805  is executed for determining whether the voice signal is abnormal according to the first sum and the second sum. If the first sum is greater, the voice signal is determined normal. Otherwise, the voice signal is determined abnormal. 
         [0046]    In addition to the aforementioned steps, the second embodiment can execute all operations of the first embodiment. People who are ordinary skilled in the art can understand corresponding steps or operations of the second embodiment according to explanations of the first embodiment and thus no unnecessary details is given here. 
         [0047]    A third embodiment of the invention is shown in  FIG. 11  which is a voice detection method used in a voice detection apparatus (such as the voice detection apparatus  3 ). In step  1100 , a voice signal is received by the receiving module  300 . Next, step  1101  is executed for dividing the voice signal into a plurality of voice frames  309  by the division module  302  and two adjacent voice frames of the voice frames overlap. Next, step  1102  is executed for comparing each of the voice frames  309  with the pre-stored normal and abnormal voice models by the likelihood value generation module  303  to generate a plurality of first likelihood values and second likelihood values, wherein the likelihood value generation module  303  comprises a characteristic retrieval module  400  and a comparison module  400 . More particularly, step  1102  comprises the steps as shown in  FIG. 12 . In step  1200 , at least one characteristic parameter  402  is retrieved from each of the voice frames by the characteristic retrieval module  400  and the characteristic parameter  402  can be one of a Mel-scale Frequency Cepstral Coefficients (MFCC), a Linear Predictive Cepstral Coefficient (LPCC), and a cepstral of the voice signal, or a combination thereof. In step  1201 , the pre-stored normal and abnormal voice models  308  are taken out from the database  304  by the comparison module  401  to perform the likelihood comparison with the characteristic parameter  402  of each of the voice frames to generate the first likelihood values  310  and the second likelihood values  311 , respectively. More particularly, a whole Gaussian mixture density function mainly consists of M component densities, wherein each of the M component densities can be defined by three parameters: a mean vector, a covariance matrix and a mixture weight. In the invention, both a normal voice (the background voice) and an abnormal voice have a corresponding GMM model λ which is a set of all the parameters as shown in the following equation: 
         [0000]      λ={w i ,u i ,Σ i }, i=1 . . . M 
         [0000]    wherein w i  represents the mixture weight, μ i  represents the mean vector, Σ i  represents the covariance matrix, and M represents the number of a Gaussian distribution. The Gaussian mixture density is a weighted sum of M component densities (i.e., λ) as shown in the following equation: 
         [0000]    
       
         
           
             
               p 
                
               
                 ( 
               
                
               x 
                
               
                  
                 λ 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 M 
               
                
               
                 
                   w 
                   i 
                 
                  
                 
                   
                     b 
                     i 
                   
                    
                   
                     ( 
                     x 
                     ) 
                   
                 
               
             
           
         
       
     
         [0000]    wherein x is a random vector in D dimensions or a characteristic vector of one voice frame in D dimensions, b i (x), i=1, . . . , M is component densities, w i , i=1, . . . , M is mixture weights satisfying a limitation that a summation of all M mixture weights should be 1, i.e., 
         [0000]    
       
         
           
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 M 
               
                
               
                 w 
                 i 
               
             
             = 
             1. 
           
         
       
     
         [0048]    Each of the component densities b i (x), i=1, . . . , M is the D dimensional Gaussian density function as shown in the following equation: 
         [0000]    
       
         
           
             
               
                 
                   b 
                   i 
                 
                  
                 
                   ( 
                   x 
                   ) 
                 
               
               = 
               
                 
                   1 
                   
                     
                       
                         ( 
                         
                           2 
                            
                           
                               
                           
                            
                           π 
                         
                         ) 
                       
                       
                         D 
                         / 
                         2 
                       
                     
                      
                     
                       
                          
                         
                           ∑ 
                           i 
                         
                          
                       
                       
                         1 
                         / 
                         2 
                       
                     
                   
                 
                  
                 exp 
                  
                 
                   { 
                   
                     
                       - 
                       
                         1 
                         2 
                       
                     
                      
                     
                       
                         ( 
                         
                           x 
                           - 
                           
                             μ 
                             i 
                           
                         
                         ) 
                       
                       T 
                     
                      
                     
                       
                         ∑ 
                         i 
                         
                           - 
                           1 
                         
                       
                        
                       
                         ( 
                         
                           x 
                           - 
                           
                             μ 
                             i 
                           
                         
                         ) 
                       
                     
                   
                   } 
                 
               
             
             , 
             
               
 
             
              
             
               i 
               = 
               1 
             
             , 
             … 
              
             
                 
             
             , 
             M 
           
         
       
     
         [0000]    wherein μ i  is the mean vector and Σ i  is the covariance matrix. 
         [0049]    Assuming that λ 1  and λ 2  respectively represent a GMM model for a normal voice and a GMM model for an abnormal voice, and x i  represents a sequence of voice frames, a plurality of likelihood values A and a plurality of likelihood values B are generated after performing the likelihood calculation on each of the voice frames based on λ 1  and λ 2  i.e., based on the equation 
         [0000]    
       
         
           
             
               
                 p 
                  
                 
                   ( 
                 
                  
                 x 
                  
                 
                    
                   λ 
                   ) 
                 
               
               = 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   M 
                 
                  
                 
                   
                     w 
                     i 
                   
                    
                   
                     
                       
                         b 
                         i 
                       
                        
                       
                         ( 
                         x 
                         ) 
                       
                     
                     . 
                   
                 
               
             
             , 
           
         
       
     
         [0000]    After performing a logarithm operation on the likelihood A and B, a plurality of likelihood log values C and a plurality of likelihood log values D are obtained. The likelihood log values C and D are the first likelihood values  310  and the second likelihood values  311 , wherein the first likelihood values  310  are the results of performing the likelihood comparison on the normal voice model and the characteristic parameter  402 , and the second likelihood values  311  are the results of performing the likelihood comparison on the abnormal voice model and the characteristic parameter  402 . 
         [0050]    Next, step  1103  is executed for deciding a window size by the decision module  305 . More particularly, the decision module  305  comprises a first calculation module  500  and a second calculation module  501  as shown in  FIG. 13 . Step  1103  comprises the following steps. In step  1300 , the first likelihood values  310  and second likelihood values  311  are accumulated respectively by the first calculation module  500  based on a predetermined minimum window in order to generate the window size  312 . As shown in  FIG. 6 , since the voice signal  301  has a length of 10 seconds, and the size of the voice frame and the size of a minimum window  600  are 5 ms and 100 ms, respectively. Step  1300  accumulates the 20 first likelihood values  310  and the 20 second likelihood values  311  that locate from the beginning to 100 ms and takes the difference of the accumulation results of the first likelihood values  310  and the second likelihood values  311  to generate the minimum window likelihood differential value  502 . 
         [0051]      FIG. 7  shows how to derive the window size in step  1301 . As aforementioned, the first weighting linear equation M 1  and the second weighting linear equation M 2  in  FIG. 7  are shown as follows: 
         [0000]    
       
         
           
             
               
                 M 
                 1 
               
                
               
                 ( 
                 N 
                 ) 
               
             
             = 
             
               { 
               
                 
                   
                     
                       
                         
                             
                            
                           1 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≤ 
                             
                               N 
                               1 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           
                             
                               
                                 N 
                                 2 
                               
                               - 
                               N 
                             
                             
                               
                                 N 
                                 2 
                               
                               - 
                               
                                 N 
                                 1 
                               
                             
                           
                         
                       
                       
                         
                             
                            
                           
                             
                               N 
                               1 
                             
                             ≤ 
                             N 
                             ≤ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           0 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≥ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                   
                    
                   
                     
 
                   
                    
                   
                     
                       M 
                       2 
                     
                      
                     
                       ( 
                       N 
                       ) 
                     
                   
                 
                 = 
                 
                   { 
                   
                     
                       
                         
                             
                            
                           0 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≤ 
                             
                               N 
                               1 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           
                             
                               N 
                               - 
                               
                                 N 
                                 1 
                               
                             
                             
                               
                                 N 
                                 2 
                               
                               - 
                               
                                 N 
                                 1 
                               
                             
                           
                         
                       
                       
                         
                             
                            
                           
                             
                               N 
                               1 
                             
                             ≤ 
                             N 
                             ≤ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                             
                            
                           1 
                         
                       
                       
                         
                             
                            
                           
                             N 
                             ≥ 
                             
                               N 
                               2 
                             
                           
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0052]    Assuming that the N derived in step  1300  equals to 480 by utilizing the aforementioned first weighting linear equation M 1  and the second weighting linear equation M 2 , step  1301  is executed for deriving that M 1 (N) is 0.4 and M 2 (N) is 0.6. 
         [0053]    Furthermore, the number of the voice frames N can be substituted into the following linear equation to derive parameters f 1 (N) and f 2 (N): 
         [0000]        f   1 ( N )= a   1   ·N+b   1    
         [0000]        f   2 ( N )= a   2   ·N+b   2    
         [0000]    wherein a 1 , a 2 , b 1  and b 2  are a predetermined constants, and the settings of a 1 , a 2 , b 1  and b 2  constants should make f 1 (N) larger and f 2 (N) smaller, In other words, f 1 (N) is a larger window value and f 2 (N) is a smaller window value. Then, step  1301  is executed for deriving the window size  312  according to the following equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     window 
                      
                     
                         
                     
                      
                     size 
                   
                   = 
                   
                     
                       
                         
                           
                             M 
                             1 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                         · 
                         
                           
                             f 
                             1 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                       
                       + 
                       
                         
                           
                             M 
                             2 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                         · 
                         
                           
                             f 
                             2 
                           
                            
                           
                             ( 
                             N 
                             ) 
                           
                         
                       
                     
                     
                       
                         
                           M 
                           1 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                       + 
                       
                         
                           M 
                           2 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                   
                 
               
             
             
               
                 
                   = 
                   
                     
                       0.4 
                        
                       
                         
                           f 
                           1 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                     + 
                     
                       0.6 
                        
                       
                         
                           f 
                           2 
                         
                          
                         
                           ( 
                           N 
                           ) 
                         
                       
                     
                   
                 
               
             
           
         
       
     
         [0054]    By utilizing the equation to derive the window size  312 , the window size value is a relatively larger while the minimum window likelihood differential value N is a smaller value. On the contrary, the derived window size value is a relatively smaller value while the minimum window likelihood differential value N is a larger value. The window size  312  is the size of the decision window  601  in  FIG. 6 . 
         [0055]    Refer back to  FIG. 11 . After the window size  312  is obtained, step  1104  is executed for accumulating the first likelihood values and the second likelihood values of the voice frames inside the window size by the accumulation module  306  to generate a first sum  313  and a second sum  314 , respectively. Step  1105  is executed for determining whether the voice signal is abnormal according to the first sum  313  and the second sum  314  by the determination module  307 . If the first sum  313  is greater, the voice signal  301  is determined normal. Otherwise, the voice signal  301  is determined abnormal. 
         [0056]    In addition to the aforementioned steps, the third embodiment can execute all operations of the first embodiment. People who are ordinary skilled in the art can understand corresponding steps or operations of the third embodiment according to explanations of the first embodiment and thus no unnecessary details is given here. 
         [0057]    The above-mentioned methods may be implemented via an application program which stored in a computer readable medium. The computer readable medium can be a floppy disk, a hard disk, an optical disc, a flash disk, a tape, a database accessible from a network or any storage medium with the same functionality that can be easily thought by people skilled in the art. 
         [0058]    While the environment voice or background voice of a voice signal has a significant change, the invention can dynamically adjust the window size for decreasing the false possibility of the detection so that the response is instant and correct. Especially for the security assurance applications, the invention can detect an abnormal voice more precisely so a real-time response can be transmitted to a security service office in time. 
         [0059]    The above disclosure is related to the detailed technical contents and inventive features thereof. People skilled in this field may proceed with a variety of modifications and replacements based on the disclosures and suggestions of the invention as described without departing from the characteristics thereof. Nevertheless, although such modifications and replacements are not fully disclosed in the above descriptions, they have substantially been covered in the following claims as appended.