On tracking noise with linear dynamical system models

This paper investigates the use of higher-order autoregressive vector predictors for tracking the noise in noisy speech signals. The autoregressive predictors form the state equation of a linear dynamical system that models the spectral dynamics of the noise process. Experiments show that the use of such models to track noise can lead to large gains in recognition performance on speech compensated for the estimated noise. However, predictors of order greater than 1 are not observed to improve the performance beyond that obtained with a first-order predictor. We analyze and explain why this is so.