Adding a Zero-Crossing Count to Spectral Information in Template-Based Speech Recognition

Abstract

Zero-crossing data can provide important feature information about an utterance which is not available in a purely spectral representation. This report describes the incorporation of zero-crossing information into the spectral representation used in a template-matching system (CICADA). An analysis of zero-crossing data for an extensive (2880 utterance, 8 talker) alpha-digit data base is described. On the basis of this analysis, a zero-crossing algorithm is proposed. The algorithm was evaluated using a confusible subset of the alpha-digit vocabulary (the E-set ). Inclusion of zero-crossing information in the representation leads to a 10-13% reduction in error rate, depending on the spectral representation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1982
Accession Number
ADA123339

Entities

People

  • Alexander H. Waibel
  • Alexander I. Rudnicky
  • Neeraja Krishnan

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Automated Speech Recognition
  • Coefficients
  • Computer Science
  • Crossings
  • Data Sets
  • Databases
  • Errors
  • Feature Extraction
  • Information Science
  • Recognition
  • Signal Processing
  • Spectra
  • Speech
  • Statistics
  • Word Recognition

Readers

  • Chemistry (specifically Chemical Fluorescence)
  • Computer Vision.

Technology Areas

  • AI & ML