TRAINING METHOD OF THE EXCITATION CODEBOOKS FOR CODE-EXCITED LINEAR PREDICTION

This paper considers the codebook for low bit-rate speech coding by co de-excited linear prediction (CELP) and the mapping table for the chan nel code. It also proposes an optimization procedure for sequential tr aining based on the structured excitation codebook. In the discussions of structurization of the codebook, the conjugate structure to repres ent the output by the sum of two vectors, the pitch synchronized proce ssing, rotating use of the codebook, the time-domain sloped gain, and the integrated mapping to the channel code are considered. The distort ion 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 sp eech is improved by approximately 1 dB. The improvement in speech qual ity 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 c onsidering bit error.