Improved speech quality and efficient vector quantization in SELP

A SELP (stochastically excited linear prediction) algorithm consisting of a two-stage vector quantization using an adaptive codebook and a stochastic codebook is described. The adaptive codebook quantization is similar to a closed-loop long-term prediction filter if the predictor delay is more than one frame length. The performance of the adaptive codebook procedure is improved by extending its codebook to include candidate vectors constructed from past synthetic excitation displaying a high level of periodicity. An algorithm is introduced which through increased symmetry of the error criterion significantly reduces the computational effort required for the search through the adaptive codebook. The algorithm can also be used for stochastic codebooks consisting of overlapping candidate vectors. It is shown that for stochastic codebooks in which neighboring candidates overlap for all but two samples, the quantization performance is as high as for codebooks containing fully independent candidate vectors