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Browse for Technologies Software PLP and RASTA in Matlab - Academic
Browse for Technologies Software PLP and RASTA in Matlab - Academic
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PLP and RASTA in Matlab - Academic |
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One of the first decisions in any pattern recognition system is the choice of what features to use: How exactly to represent the basic signal that is to be classified, in order to make the classification algorithm's job easiest. Speech recognition is a typical example. Through more than 30 years of recognizer research, many different feature representations of the speech signal have been suggested and tried. The most popular feature representation currently used is the Mel-frequency Cepstral Coefficients or MFCC. Another popular speech feature representation is known as RASTA-PLP, an acronym for Relative Spectral Transform - Perceptual Linear Prediction. PLP was originally proposed by Hynek Hermansky as a way of warping spectra to minimize the differences between speakers while preserving the important speech information [Herm90]. RASTA is a separate technique that applies a band-pass filter to the energy in each frequency subband in order to smooth over short-term noise variations and to remove any constant offset resulting from static spectral coloration in the speech channel e.g. from a telephone line [HermM94]. |
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