A fast encoding technique is described for vector quantization (VQ) of line spectral frequency parameters. A reduction in VQ encoding complexity is achieved by using a preliminary test that reduces the necessary codebook search range. The test is performed based on two criteria. One criterion uses the distance between a specific single element of the input vector and the corresponding element of the codevectors in the codebook. The other criterion makes use of the ordering property of LSF parameters. The fast encoding technique is implemented in the enhanced variable rate codec (EVRC) encoding algorithm. Simulation results show that the average searching range of the codebook can be reduced by 44.50% for the EVRC without degradation of spectral distortion (SD).

In this paper, the use of optimal Karhunen-Loeve (KL) transform for quantization of speech line spectrum frequency (LSF) coefficients is studied. Both scalar quantizer (SQ) and vector quantizer (VQ) schemes are developed to encode efficiently the transform parameters after operating one or two-dimensional KL transform. Furthermore, the SQ schemes are also combined with entropy coding by using Huffman variable length coding (VLC). The basic idea in developing these schemes is utilizing the strong correlation of LSF parameters to reduce the bit rate for a given level of fidelity. Since the use of global statistics for generating the coding scheme may not be appropriate, we propose several adaptive KL transform systems (AKL) to encode the LSF parameters. The performance of all systems for different bit rates is investigated and adequate comparisons are made. It is shown that the proposed KL transform coding systems introduce as good as or better performance for both SQ and VQ in the examined bit rates compared to other methods in the field of LSF coding.