Split vector quantization of LSF parameters with minimum of dLSF constraint

In speech coding, the spectral envelope of an analysis frame is often represented by line spectral frequencies (LSFs). LSFs are estimated from given linear predictive coefficients (LPCs) and can be transformed back to corresponding LPCs without loss of information. The authors present two improved split vector quantization (SVQ) methods for line spectral frequency (LSF) parameters. By using these methods jointly, the codewords and quantization results conserve a given minimum difference LSF (dLSF), although they are trained and quantized with a weighted distance measure. Experimental results show that the proposed methods are more effective than conventional SVQ methods, because the total training error and number of outliers due to quantization are all reduced.