For text-dependent speaker recognition, improved
performance has been found by introducing ceps-
tral derivatives into the feature space as it captures
the transitional information in the speech. Cepstral
weighting or liftering is also used which enhances
the speaker recognition. Cepstral mean subtrac-
tion (CMS) technique also significantly improves
the performance of a recognition system in which
training is done on one channel condition while
testing is done on another channel condition. An-
other technique, known as Pole-filtered Cepstral
Mean Subtraction (PFCMS), modifies the LP poles
so as to broaden the bandwidth of the formant
poles. The cepstrum formed from these modified
poles has less speech information and more chan-
nel information. It has noticed that PFCMS outper-
forms CMS in speaker-identification problem.
The relative spectral (RASTA) technique takes ad-
vantage of the fact that the rate of change of nonlin-
guistic components in speech often lies outside the
typical rate of change of the vocal-tract shape.
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