Adaptive identification of a time-varying ARMA speech model

We propose an adaptive algorithm to estimate time-varying ARMA parameters for speech signals. It estimates both input excitations and underlying system parameters. The proposed algorithm is an extended form of the Kalman filter algorithm. We assume the input is either a white Gaussian process or a pseudoperiodical pulse-train as commonly adopted in LPC processing. The time variation of parameters is monitored by a likelihood function. In order to estimate optimal parameters in a small amount of data, AR and MA orders of an estimator are set to be higher than those of a true system. Parsimonious ARMA parameters are calculated from parameters obtained by the high-order ARMA model. Examples of synthetic and real speech sounds are given to demonstrate the tracking ability of this algorithm.