This paper presents a new general distance measure that not only can be used in a vector quantization (VQ) of line spectrum frequency (LSF) parameters but also performs well in a LSF transformed domain. The new distance is based on the spectral sensitivity of LSFs and their transformed coefficients. In addition, a fix scaling vector is used to decrease the sensitivity of spectral error at higher frequencies. Experimental results have shown that the proposed distance measure leads to as good as or better performance of VQ compared to other methods in the field of LSF coding. The use of this distance as the weighting function of the LSF transformed parameters is also suggested.

In this paper, the correlation properties are used to develop two efficient encoding schemes for speech line spectrum frequency (LSF) parameters. The first scheme (1D KL), which exploits the intraframe correlation, is based on one-dimensional Karhunen-Loeve (KL) transformation; the second scheme, which requires some coding delays to further utilize the interframe correlation, uses two-dimensional (2D KL) transform in the frequency domain or one-dimensional KL transform co-operating with DPCM in the time domain. Moreover, since the KL transform is globally optimal, which is sensitive to the change of input data statistics, further two adaptive transform coding systems are also investigated in this paper. The performance of all systems for different bit rates is investigated and adequate comparisons are made. It is shown that the gain of using KL transformation to exploit the intraframe and interframe correlation is 3 and 4 bits/speech frame, respectively.