Estimation and vector quantization of noisy speech

The block and alphabet-constrained formulations are compared for the problem of vector quantization of noisy speech. In the optimum estimator/source-coder structures, a training mode vector quantizer due to Linde et al. (1980) is used as the source coder for the estimator outputs in all cases, and three block estimators and five alphabet-constrained estimators are examined. Objective and subjective performance results are obtained for all eight estimators used in conjunction with training mode vector quantizers at a rate of 1 bit/dimension, for dimensions 1,2,. . . ,8, and 2 bits/dimension, for dimensions 1,2,3, and 4, on five sentences, The results show the superiority of the alphabet-constrained approach, using the frame-adaptive Kalman filter, with improvements in output signal-to-noise ratio over the block approach of 20%