VECTOR QUANTIZATION AND SCALAR LINEAR PREDICTION FOR WAVEFORM CODING OF SPEECH AT 16 kbls

This thesis is an investigation of Vector Quantization, Scalar Linear Prediction and other related signal processing techniques, with the purpose of providing high quality, low delay speech waveform coding at medium data rates (16 kbls). Speech waveform coding systems based on adaptive scalar prediction and adaptive scalar quantization have been used to provide toll quality coded speech at high rates such as 32 kbls (ADPCM). However, the performance of these systems is known to degrade to sub-toll quality at 16 kbls, due to excessive quantization noise. Vector Quantization (VQ) is well known to provide a significant reduction in quantization noise over scalar quantization; in fact VQ can be shown to have a theoretically optimal rate-distortion performance at very large vector dimensions. This suggests that the performance of 16 kbls ADPCM may be sigmf5cantly improved by replacing the scalar quantizer with a vector quantizer. The resulting configuration, called Vector ADPCM, has an inherently high complexity; however, techniques are described which reduce the complexity to the level where implementation with commercially available digital hardware is feasible. Vector ADPCM is found to provide a 3-dB performance improvement over scalar ADPCM, with a 15 times increase in complexity, while still maintaining an encodingldecoding delay of less than 2 milliseconds. Adaptive Postfiltering significantly improves the subjective quality of the coded speech. Informal listening tests indicate that the coded speech is of very good communications quality.