PATTERN RECOGNITION, FUNCTIONALS, AND ENTROPY.

Abstract

Pattern recognition (including sound recognition) is described mathematically as the problem to compute for any element of a given class its image in a classification set. The difficulty lies in the fact that the map may be implicitly defined by a property or must be extrapolated from prototypes. An entropy measure and an equivocation measure are defined that permit an assessment of the improvement gained (and the price in confusion paid) by a set of features. Linear 'features' are identified as measures and L superscript 2 functions respectively. It is shown that certain important normalizations (position, size, pitch, etc.) are non-linear operations. Finally, the method of spectral analysis which is widely used for speech analysis is examined critically. It is shown that contrary to common belief Fourier analysis is not very suitable for detecting certain speech particles (consonants, stops, etc.). (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1967
Accession Number
AD0659197

Entities

People

  • Hans J. Bremermann

Organizations

  • University of California, Berkeley

Tags

DTIC Thesaurus Topics

  • Classification
  • Consonants
  • Fourier Analysis
  • Identification
  • Neurobehavioral Manifestations
  • Particles
  • Pattern Recognition
  • Prototypes
  • Recognition
  • Speech Analysis

Readers

  • Graph Algorithms and Convex Optimization.
  • Speech Processing/Speech Recognition.
  • Theoretical Analysis.

Technology Areas

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