"Contemporary Methods for Speech Parameterization" offers a
general view of short-time cepstrum-based speech parameterization
and provides a common ground for further in-depth studies on the
subject. Specifically, it offers a comprehensive description,
comparative analysis, and empirical performance evaluation of
eleven contemporary speech parameterization methods, which compute
short-time cepstrum-based speech features.
Among these are five discrete wavelet packet transform
(DWPT)-based, six discrete Fourier transform (DFT)-based speech
features and some of their variants which have been used on the
speech recognition, speaker recognition, and other related speech
processing tasks. The main similarities and differences in their
computation are discussed and empirical results from performance
evaluation in common experimental conditions are presented. The
recognition accuracy obtained on the monophone recognition,
continuous speech recognition and speaker recognition tasks is
contrasted against the one obtained for the well-known and widely
used Mel Frequency Cepstral Coefficients (MFCC).
It is shown that many of these methods lead to speech features
that do offer competitive performance on a certain speech
processing setup when compared to the venerable MFCC. The last does
not target the promotion of certain speech features but instead
aims to enhance the common understanding about the advantages and
disadvantages of the various speech parameterization techniques
available today and to provide the basis for selection of an
appropriate speech parameterization in each particular case.
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