Therefore, it suppresses the Spectral components
that change more slowly or quickly than the typi-
cal rate of change of speech. The RASTA approach
can be combined with the PLP method to get the
LP transfer function Hz [7]. Unlike cepstral mean
subtraction, which removes the dc component of
the short-term log spectrum, RASTA processing
influences the speech spectrum in a more complex
manner and emphasizes spectral transitions. The
use of RASTA processing has been shown to im-
prove speech-recognition performance under mis-
matched environments. This band pass operation,
combined with BPL filtering, has been shown to im-
prove speaker-recognition performance under mis-
matched conditions.
However, this is not the end, currently speech rec-
ognition communities are working on GMM and
HMM, Long Short-term Memory RNNs based ap-
proach as well as deep neural network which takes
the speaker recognition to the next level and
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