The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 06, 1990
Filed:
Jun. 26, 1987
Richard H Ketchum, Wheaton, IL (US);
Willem B Kleijn, Batavia, IL (US);
Daniel J Krasinski, Glendale Heights, IL (US);
American Telephone and Telegraph Company, New York, NY (US);
AT&T Bell Laboratories, Murray Hill, NJ (US);
Abstract
Apparatus for encoding speech using a code excited linear predictive (CELP) encoder using a recursive computational unit. In response to a target excitation vector that models a present frame of speech, the computational unit utilizes a finite impulse response linear predictive coding (LPC) filter and an overlapping codebook to determine a candidate excitation vector from the codebook that matches the target excitation vector after searching the entire codebook for the best match. For each candidate excitation vector accessed from the overlapping codebook, only one sample of the accessed vector and one sample of the previously accessed vector must have arithmetic operations performed on them to evaluate the new vector rather than all of the samples as is normal for CELP methods. For increased performance, a stochastically excited linear predictive (SELP) encoder is used in series with the adaptive CELP encoder. The SELP encoder is responsive to the difference between the target excitation vector and the best matched candidate excitation vector to search its own overlapping codebook in a recursive manner to determine a candidate excitation vector that provides the best match. Both of the best matched candidate vectors are used in speech synthesis.