Trellis code excited linear prediction (TCELP) speech coding

Abstract

This paper describes extensions to trellis-based scalar-vector quantization (TB-SVQ) , as novel techniques for coding sources with memory. These techniques are proposed as efficient solutions for the quantization of the excitation in Code-Excited Linear Predictive (CELP) coding algorithms. A new 24 kbit/s low-delay trellis CELP (LD-TCELP) coder is introduced which achieves high-performance coding of narrowband speech with an algorithmic delay of only 3 ms. This coder utilizes the granular structure of the underlying trellis code and the codebook shaping capability of the SVQ approach to effectively capitalize on the gain of higher dimensional (24-D) vector quantization. We present experimental results which show that the LD-TCELP coder outperforms two toll-quality standardized coders: ITU-T G.728 16 kbit/s LD-CELP and ITU-T G.726 32 kbit/s ADPCM. The proposed LD-TCELP coder can also utilize a low-complexity codebook search approach with a computational complexity that is about 75% of that of ITU-T G.728 LD-CELP.