A linear predictive method for highly compressed presentation of speech spectra

Our study proposes a new linear predictive algorithm, Linear Prediction with Sample Grouping (LPSG), for spectral modelling of speech. This method reformulates computation of linear prediction by grouping and extrapolating samples used in the prediction. In LPSG the number of samples used in the computation of the prediction is larger than the number of parameters to define the optimal predictor. Consequently, the proposed method makes it possible to obtain all-pole models for speech spectra that can be defined with a very compressed set of parameters. Quantisation of the prediction parameters of LPSG was compared in the present study to conventional linear prediction (LP) using a very low order of prediction. It appeared that LPSG yields better spectral matching and smaller residual energies in comparison to LP