Vocal dysperiodicities estimation by means of adaptive long-term prediction

An adaptive formulation of the long-term bidirectional linear predictive analysis is proposed in the context of the acoustic assessment of disordered speech. Vocal dysperiodicities are summarized by means of a signal-to-dysperiodicity ratio (SDR) marker. It is shown that performing an adaptive forward and backward long-term linear prediction of each speech sample and retaining the minimal prediction error energy as a cue of vocal dysperiodicity results in an SDR that correlates with the perceived degree of hoarseness. The coefficients of the time-varying long-term linear predictive model are estimated by means of the recursive least squares algorithm. The corpora comprise sustained vowels and French sentences produced by male and female normophonic and dysphonic speakers. A perceptual assessment of speech samples, which rests on comparative judgments, is used to evaluate the ability of the acoustic marker to predict subjective measures of voice quality. Experimental results show that the adaptive approach gives rise to high correlations for sustained vowels as well as for sentences.