Distance Measures for Speech Recognition

Abstract

This report is concerned with the application of aspects of statistical pattern classification to speech recognition. It presents an extension of linear discriminant analysis to the case where the classes are unknown. This extension provides solutions to the interrelated problems of the design of acoustic representations and spectral distance measures, and allows the efficient combination of heterogeneous sets of parameters. In particular, a representation called IMELDA based on the output of the filter-bank and its changes in time is introduced. Other approaches to distance measures are discussed. It is noted that these other methods lack the ability to make efficient combinations of heterogeneous parameters, and that they require empirical adjustments in order to give good results. Tests indicate that IMELDA provides markedly superior recognition performance compared to the alternatives. Speech recognition; Canada; Pattern recognition.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1989
Accession Number
ADA207710

Entities

People

  • C. Lefebvre
  • M. J. Hunt

Organizations

  • National Research Council Canada

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Computer Programming
  • Covariance
  • Data Science
  • Databases
  • Discriminant Analysis
  • Dynamic Programming
  • Information Science
  • Materials
  • Normal Distribution
  • Pattern Recognition
  • Power Spectra
  • Probability
  • Recognition
  • Signal Processing
  • Spectra

Readers

  • Computer Vision.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms