Low cost vector quantization methods for spectral coding in low rate speech coders

Abstract

In low rate speech coders based on the linear prediction method, the quality of synthesized speech can be improved by enhancement of the short-term spectrum quantization stage. In this study, we propose two new efficient methods for coding the spectral parameters, namely sorted codebook vector quantization (SCVQ) and fine-coarse vector quantization (FCVQ). The principles of these methods are presented along with the methods of training and optimizing the related codebooks. The performance of the new schemes is compared experimentally with other efficient methods, such as tree-searched vector quantization (TSVQ) and multi-stage vector quantization (MSVQ). We demonstrate that the new methods offer significant cost reduction whilst achieving superior quality.