Tree and trellis coding of speech and stationary speech-like signals

Abstract

This paper deals with the application of tree and trellis coding concepts to the coding of stationary first-order auto regressive sources and speech signals at a rate of 1 bit/sample. We show first that a probabilistically generated tree (or trellis) performs significantly better than a tree (or trellis) that is generated in a deterministic way. We show then that the segmental signal-to-noise-ratios of con ventional speech coding schemes (including) th ose with adaptive quantization and/or p�ediction) can be increased by about 2 dB. Slightly higher values of 3 to 4 dB have been realized with schemes whi�h employ backward-adaptive quantization.