Accession Number : AD0776594

Title :   A Hilbert Space Approach to Linear Predictive Analysis of Speech Signals.

Descriptive Note : Technical rept.,

Corporate Author : STANFORD UNIV CALIF STANFORD ELECTRONICS LABS

Personal Author(s) : Narasimha,Madihally J. ; Shenoi,Kishan ; Peterson,Allen M.

Report Date : FEB 1974

Pagination or Media Count : 41

Abstract : Linear predictive analysis is geometrically interpreted to provide insight into the various formulations prevalent in current literature. The procedure for obtaining the predictive filter coefficients is considered as a minimum norm problem in an appropriate Hilbert space. Application of the projection theorem using specific sets of bases yields the normal equations for the covariance and autocorrelation methods. Orthogonalization of the basis vectors leads to the popular ladder structure and yields a recursive algorithm for evaluating the predictor and PARCOR coefficients. (Author)

Descriptors :   *Information theory, *Speech recognition, *Hilbert space, Entropy, Coding, Data compression, Recursive functions, Estimates

Subject Categories : Cybernetics
      Voice Communications

Distribution Statement : APPROVED FOR PUBLIC RELEASE