A fast determination of stochastic excitation without codebook search in CELP coder

The major drawback of the code excitation linear prediction (CELP) coder is computational complexity that finds the best excitation vector from a stochastic codebook. To provide a synthesized speech signal with reasonable quality, the size of the stochastic codebook should be large. For this reason, the search becomes highly complex. To overcome this difficulty, several methods have been proposed. In this paper, we consider a method that enables us to directly determine the stochastic excitation vector without a codebook search. The stochastic excitation vector has been determined by projection onto a subspace that is obtained using the Karhunen-Loeve (K-L) expansion and the spectral property of the random excitation residual vector. Since the excitation vector can be determined without a codebook search, the computational complexity becomes low. From experimental results, it is shown that the proposed coder provides a synthesized speech signal that is quite comparable in quality to that of the conventional CELP coder with low computational complexity