KLT-based classified VQ for the speech signal

If the signal statistics are given, direct vector quantization (DVQ) according to these statistics provides the highest coding efficiency, but requires unmanageable storage requirements. In. code-excited linear predictive (CELP) coding. a single “compromise” codebook is trained in the prediction residual-domain and the space-filling and shape advantages of vector quantization (VQ) are utilized in a non-optimal, average sense. In this paper. we propose a Karhunen-Loève Transform (KLT)-based classified VQ (CVQ), where the space-filling advantage can be utilized since the Voronoi-region shape is not affected by the KLT. The memory and shape advantages can be also used, since each codebook is designed based on a narrow class of KL T -domain statistics. Our experiments show that the KLT-CVQ provides a higher SNR than CELP and (single-codebook) DVQ, and has a computational complexity similar to DVQ and much lower than CELP. Storage requirements are modest because of the energy concentration property of the KL T.