Frédéric Bettens

Estimation of vocal dysperiodicities in disordered connected speech by means of distant-sample bidirectional linear predictive analysis

The article presents an analysis of vocal dysperiodicities in connected speech produced by dysphonic speakers. The processing is based on a comparison of the present speech fragment with future and past fragments. The size of the dysperiodicity estimate is zero for periodic speech signals. A feeble increase of the vocal dysperiodicity is guaranteed to produce a feeble increase of the estimate. No spurious noise boosting occurs owing to cycle insertion and omission errors, or phonetic segment boundary artifacts. Additional objectives of the study have been investigating whether deviations from periodicity are larger or more commonplace in connected speech than in sustained vowels, and whether sentences that comprise frequent voice onsets and offsets are noisier than sentences that comprise few. The corpora contain sustained vowels as well as grammatically- and phonetically matched sentences. An acoustic marker that correlates with the perceived degree of hoarseness summarizes the size of the dysperiodicities. The marker values for sustained vowels have been highly correlated with those for connected speech, and the marker values for sentences that comprise few voiced/unvoiced transients have been highly correlated with the marker values for sentences that comprise many.

Vocal dysperiodicities estimation by means of adaptive long-term prediction

An adaptive formulation of the long-term bi-directional 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.