A geostatistical model for linear prediction analysis of speech

This paper presents a geostatistical model as a new approach to the linear prediction analysis of speech. The autocorrelation method of autoregressive modeling, which is widely applied in the linear predictive coding of speech, is used as a benchmark for comparison with the present algorithm. Before discussing the proposed model, we will briefly describe the concepts of linear prediction analysis of speech and how this is solved by the well-known method of autocorrelation. Following is the introduction of geostatistics including the ideas of regionalized variables, semi-variograms and kriging equations. We then propose a geostatistical model to the linear prediction modeling of speech signals. Examples on speech data are given to illustrate the effectiveness of the present algorithm in comparison with the autocorrelation method. Advantages offered by the proposed geostatistical algorithm over the autocorrelation method in the linear prediction analysis of speech are summarized as follows: (1) it is more effective due to the optimization of the kriging equations taking into account the biased condition; (2) it is more flexible by allowing different biased values for the fitting of the signal spectrum, and therefore may provide a means for adaptive LPC; (3) it can give a good estimate of the number of poles used in the LPC by means of the theoretical semi-variogram. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.