All-pole modelling of mixed excitation signals

Conventional linear prediction (LP) techniques can fail to adequately model speech spectra when the model order is too low and/or when the input is periodic (voiced speech). We view the LP modelling problem as a correlation matching problem. We introduce a correlation matching criterion which models the signal as a filtered mixture of a noise-like excitation and a periodic excitation. As such it is an extension of the discrete all-pole (DAP) modelling approach. The new technique provides a means to generate LP spectra that evolve more smoothly from frame to frame even when the excitation signal has a periodic component with changing period.