On adaptive vector transform quantization for speech coding

Adaptive vector transform quantization (AVTQ) as a coding system is discussed. The optimal bit assignment is derived based on vector quantization asymptotic theory for different PDFs (probability density functions) of the transform coefficients. Strategies for shaping the quantization noise spectrum and for adapting the bit assignment to the changes in the speech statistics are discussed. A good estimate of the efficiency of any coding system is given by the system coding gain over scalar PCM (pulse code modulation). Based on the optimal bit allocation, the coding gain of the vector transform quantization (VTQ) system operating on a stationary input signal is derived. The VTQ coding gain demonstrates a significant advantage of vector quantization over scalar quantization within the framework of transform coding. System simulation results are presented for a first-order Gauss-Markov process and for typical speech waveforms. The results of fixed and adaptive systems are compared for speech input. Also, the AVTQ results are compared to known scalar speech coding systems