Low-order model for speech signals

Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some classical methods. The AR model is then reduced using the state projection method, operating in the state space. The model reduction yields a reduced-order autoregressive moving average (ARMA) model which interestingly preserves the key properties of the original full-order model such as causality, stability, minimality, and phase minimality. Line spectral frequencies LSF and signal-to-noise ratio SNR behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some computer simulations are conducted on some practical speech segments. (C) 2004 Elsevier B.V. All rights reserved.