PERCEPTUAL LINEAR PREDICTION
BLOCK DIAGRAM

• Goals:
  • Apply greater weight to perceptually-important portions of the spectrum
    
    
  • Avoid uniform weighting across the frequency band
  
  
• Algorithm:
  • Compute the spectrum via a DFT
    
    
  • Warp the spectrum along the Bark frequency scale
    
    
  • Convolve the warped spectrum with the power spectrum of the simulated critical band masking curve and downsample (to typically 18 spectral samples)
    
    
  • Preemphasize by the simulated equal-loudness curve:
    • Simulate the nonlinear relationship between intensity and perceived loudness by performing a cubic-root amplitude compression
    
    
  • Compute an LP model
    
    
  • Compute an LP-derived cepstrum
  
  
• Claims:
  • Improved speaker independent recognition performance
    
    
  • Increased robustness to noise, variations in the channel, and microphones