A methodology for efficiency estimation of the speech signal feature extraction methods

  • Zdravko Kačič
  • Bogomir Horvat
Speech And Text
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


This paper deals with a basis of methodology for speech signal feature description. Speech signal is described by three sets of features (the set of all descriptive features, the set of all selected features, and the set of all characteristic features). Feature description methods are described by three sets of maps (descriptive feature map, selected feature map, and characteristic feature map). As an example two feature description methods are considered — zero — crossing method and method of formant frequency energy classes (variant a and b). Efficiency of a single method being used in the recognition process has been estimated on the basis of experimental results. It is shown that the Fourier transformation as a map of descriptive features is more convenient as a measurement of time interval lenght. The mapping rule in variant b of the method of formant frequency energy classes gives a more convenient map of selected features than the mapping rule in variant a. With these maps the smallest features overlapping and consequently a better average recognition accuracy (greater than 92.5 %) can be achieved.


Feature Vector Speech Signal Discrete Fourier Transformation Recognition Accuracy Descriptive Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Zdravko Kačič
    • 1
  • Bogomir Horvat
    • 1
  1. 1.Faculty of Technical SciencesUniversity of MariborMariborYugoslavia

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