For low bit rate linear predictive speech coding, it is desirable to quantize the LPC parameters using as few bits as possible with transparent quantization quality. A novel structural constrained vector quantization scheme, namely split residual vector quantization (SRVQ), is proposed. In this scheme, PARCOR coefficient vector is split into subvectors and each subvector is sequentially quantized according to a multi-stage structure based on the principle of minimizing the prediction error energies. Simulation results show that a 2-stage split residual vector quantizer can achieve transparent quantization at 25 bit/frame while autocorrelation analysis is employed to calculate the LPC parameters.>

A novel split vector quantization (SVQ) scheme for low bit rate coding of speech signals is proposed. In this scheme, the LPC parameter vector, which is represented by Parcor coefficients, is split into small-dimension subvectors, and each subvector is sequentially quantized according to a multistage structure that resembles a segmented lattice filter. The forward and backward prediction residuals in the segmented filter are coupled across VQ stages. The quantizer in each stage operates on the principle of minimizing the forward and backward prediction error energies similar to linear predictive analysis. Simulation results show that the new split VQ scheme can achieve transparent quantization of LPC parameters at 25 b/frame.>

The authors introduce a spectral analysis technique for LPC systems based on using a high-order lattice filter having only few nonzero reflection coefficients. An efficient algorithm based on dynamic programming techniques has been developed to find the nonzero coefficients and their corresponding delays for the thinned lattice filter. Simulation results show that the thinned filter approach can achieve more accurate spectrum fitting to speech signals.>