Training method of the excitation codebooks for code-excited linear prediction

Abstract

This paper considers the codebook for low bit-rate speech coding by code-excited linear prediction (CELP) and the mapping table for the channel code. It also proposes an optimization procedure for sequential training based on the structured excitation codebook. In the discussions of structurization of the codebook, the conjugate structure to represent the output by the sum of two vectors, the pitch synchronized processing, rotating use of the codebook, the time-domain sloped gain, and the integrated mapping to the channel code are considered. The distortion measure used in the training is the same closed-loop measure as is used in the coding. In other words, the distortion between the input speech and the synthesized speech at the perceptually weighted filter output is minimized. By training the codebook, the SNR of the coded speech is improved by approximately 1 dB. The improvement in speech quality is also verified. It is also verified that the robustness against bit error can be improved by the training of the mapping table to the channel code, as well as by training based on the distortion measure considering bit error.