
doi: 10.1121/1.402803
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.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
