Quantization properties of transmission parameters in linear predictive systems

Several alternate sets of parameters that represent the linear predictor are investigated as transmission parameters for linear predictive speech compression systems. Although each of these sets provides equivalent information about the linear predictor, their properties under quantization are different. The results of a comparative study of the various parameter sets are reported. Specifically it is concluded that the reflection coefficients are the best set for use as transmission parameters. A more detailed investigation of the reflection coefficients is then carried out using a spectral sensitivity measure. A method of optimally quantizing the reflection coefficients is derived using a minimax spectral error criterion and the sensitivity analysis. The method consists of transforming the reflection coefficients to the so-called log area ratios and linearly quantizing them. A qualitative study on changes in pole locations due to quantization serves to corroborate the use of this optimal quantization. An optimal bit allocation strategy for the transmission parameters is also presented. The use of another spectral sensitivity measure renders logarithms of the ratios of normalized errors associated with linear predictors of successive orders as the optimal quantization parameters. Informal listening tests indicate that the use of log area ratios for quantization leads to better synthesis than the use of log error ratios.