On encoding filter parameters for stochastic coders

Stochastic coders provide a way of encoding the excitation to the synthesis filter at bit rates of about 2 kbit/s, thus leading to fhe possibility of high quality speech coding at 4.8 kbit/s. In these coders, the excitation is encoded as an index into a codebook of random excitation waveforms and the coder transmits the parameters of a short-term filter (LPC all-pole predictor), the parameters of a long-term filter (pitch predictor) and the excitation gain to the receiver. Although the coders give excellent speech quality with unquantized parameters, the output degrades significantly when the filter parameters are coarsely quantized. For a 4.8 kbit/s coder, the short-term filter parameters have to be quantized at 1 kbit/s or less and conventional scalar quantizers at this bit rate result in severe degradation of output speech. In this paper we describe the performance of stochastic coders when the short-term filter parameters are quantized using direct vector quantization and vector quantization with predictive coding and eigenvector rotation. Our results indicate that good performance can be achieved with relatively small codebooks for the quantizers and that predictive coding with eigenvector rotation gives a small but consistent improvement over direct vector quantization.