Classified nonlinear predictive vector quantization of speech spectral parameters

Nonlinear predictive split vector quantization (NPSVQ) and classi ed NPSVQ (CNPSVQ) are introduced to exploit the correlation among the speech spectral parameters from two adjacent analysis frames. By interleaving intraframe SVQ with forward predictive SVQ, error propagation is limited to at most one adjacent frame. At an overall bit rate of about 21 bits/frame, NPSVQ can provide similar coding quality as intraframe SVQ at 24 bits/frame. Voicing classication is used in CNPSVQ to obtain an additional average gain of 1 bit/frame for unvoiced frames. Therefore, an overall bit rate of 20 bits/frame is obtained for unvoiced frames. The particular form of nonlinear prediction we use incurs virtually no additional encoding computational complexity. We have veri ed our comparative performance results using subjective listening tests.