Accession Number:

AD0659197

Title:

PATTERN RECOGNITION, FUNCTIONALS, AND ENTROPY.

Descriptive Note:

Technical rept.,

Corporate Author:

CALIFORNIA UNIV BERKELEY DEPT OF MATHEMATICS

Personal Author(s):

Report Date:

1967-08-01

Pagination or Media Count:

26.0

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

Subject Categories:

  • Acoustics
  • Bionics
  • Voice Communications

Distribution Statement:

APPROVED FOR PUBLIC RELEASE