A time-varying long-term bi-directional linear predictive analysis is proposed in the context of the acoustic assessment of disordered speech. It is shown that performing a forward and backward longterm linear prediction of each speech sample and retaining the minimal overall prediction error as a cue of vocal dysperiodicity obtains a signal-to-noise ratio that correlates with the perceived degree of hoarseness. The coefficients of the time-varying long-term linear predictive model are estimated adaptively by means of a recursive least squares algorithm.