An approach to minimum mean-squared error (MMSE) decoding for vector quantization over channels with memory is presented. The decoder is based on the Gilbert channel model that allows the exploitation of both intra- and inter-block correlation of bit error sequences. We also develop a recursive algorithm for computing the a posteriori probability of a transmitted index sequence, and illustrate its performance in quantization of Gauss-Markov sources under noisy channel conditions.

Soft bit speech decoding, as a new approach of error concealment, is applied to the mixed excitation linear prediction (MELP) algorithm. Average residual redundancies of the quantized parameters are exploited in the error concealment process as an a priori knowledge of the source. Results show a significant SNR improvement for parameters decoded using the error concealment scheme.