Variable-Dimension Vector Quantization of Speech Spectra for Low-Rate Vocoders

Optimal vector quantization of variable-dimension vectors in principle is feasible by using a set of fixed dimension VQ codebooks. However, for typical applications, such a multi-codebook approach demands a grossly excessive and impractical storage and computational complexity. Efficient quantization of such variable-dimension spectral shape vectors is the most challenging and difficult encoding task required in an important family of low bit-rate vocoders. We introduce a simple and effective formulation of variable-dimension vector quantization (VDVQ) which quantizes variable-dimension vectors using a single universal codebook having fixed dimension yet covering the entire range of input vector dimensions under consideration. This VDVQ technique is applied to quantize variable-dimension spectral shape vectors leading to a high quality speech coder at the low bit-rate of 2.5 kb/s. The combination of a universal spectral codebook and structured VQ reduces storage and computational comp..