Autoregressive models with time-dependent log area ratios

A large class of non-stationary signals, containing speech signals, but not restricted to them, can be represented by time-varying models, the coefficients of which are finite linear combinations of known time functions. Such models have been found useful for speech recognition and speech synthesis but they suffer in this last application from a lack of stability. This paper describes a time-varying AR model into which the time-dependency is coded through Log Area Ratios: this ensures its stability. Two algorithms for the estimation of these time-varying Log Area Ratios are proposed, the first one is an approximation via a lattice filter, while the second one maximises a least squares criterion.