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Optimal Grey Predictor for Speech Spectrum and Its Application to Spectral Quantization

Most of low bit-rate speech coders based on the speech production model use line spectrum frequencies (LSFs) to represent short-term spectra of speech signals. A vector predictor for the LSFs which consists of a group of grey predictors is investigated in this paper for the purpose of estimating the current LSFs accurately by using previous LSFs. We impose a new parameter called fractional step (FS) on the grey predictor which is determined by the steepest descent method in achieving the optimal prediction performance. Furthermore, the vector predictor can easily be applied to a vector predictive coder for spectral quantization. The experimental results show that the direct scalar quantization and partitioned vector quantization for the LSFs need, in total, 34 bits/frame and 27 bits/frame, respectively to achieve the spectral distortion limen (DL) of 1 dB. The proposed vector predictor with scalar quantization scheme can maintain the same spectral distortion at only 24 bits/frame.