Patent ID: 11875799
Assignee: SOUNDAI TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 2:
3. The method according to claim 2, wherein fusing the i-vector voiceprint feature, the x-vector voiceprint feature and the d-vector voiceprint feature based on the linear discriminant analysis, comprises:
calculating a within-class scatter matrix Sw:, S
    w
  
  =
  
    
      ∑
      
        i
        =
        1
      
      n
    
    ⁢
    
      
    
    ⁢
    
      
        ∑
        
          
            x
            
              (
              k
              )
            
          
          ∈
          
            D
            i
          
        
      
      ⁢
      
        
          (
          
            
              x
              
                (
                k
                )
              
            
            -
            
              μ
              i
            
          
          )
        
        ⁢
        
          
            (
            
              
                x
                
                  (
                  k
                  )
                
              
              -
              
                μ
                i
              
            
            )
          
          T
        
      
    
  

where n represents a number of a plurality of class labels, x(k) represents a sample in a subset Di, and μi represents a mean value of a subset Di;

calculating an inter-class scatter matrix Sb:, S
    b
  
  =
  
    
      ∑
      
        i
        =
        1
      
      n
    
    ⁢
    
      
    
    ⁢
    
      
        p
        ⁡
        
          (
          i
          )
        
      
      ⁢
      
        (
        
          
            μ
            i
          
          -
          μ
        
        )
      
      ⁢
      
        
          (
          
            
              μ
              i
            
            -
            μ
          
          )
        
        T
      
    
  

where n represents a number of a plurality of class labels, P(i) represents a prior probability of a sample of class i,μi represents a mean value of a subset Di, and μ represents a mean value of all samples;

calculating eigenvalues of a matrix Sw−1Sb;
finding largest k eigenvalues of the matrix Sw−1Sb and k eigenvectors (w1, w2, . . . , wk)
corresponding the largest k eigenvalues; and
projecting an original sample into a low dimensional space generated based on (w1, w2, . . . , wk) as a basis vector.