Closed phase covariance analysis based on constrained linear prediction for glottal inverse filtering.

Closed phase (CP) covariance analysis is a widely used glottal inverse filtering method based on the estimation of the vocal tract during the glottal CP. Since the length of the CP is typically short, the vocal tract computation with linear prediction (LP) is vulnerable to the covariance frame position. The present study proposes modification of the CP algorithm based on two issues. First, and most importantly, the computation of the vocal tract model is changed from the one used in the conventional LP into a form where a constraint is imposed on the dc gain of the inverse filter in the filter optimization. With this constraint, LP analysis is more prone to give vocal tract models that are justified by the source-filter theory; that is, they show complex conjugate roots in the formant regions rather than unrealistic resonances at low frequencies. Second, the new CP method utilizes a minimum phase inverse filter. The method was evaluated using synthetic vowels produced by physical modeling and natural speech. The results show that the algorithm improves the performance of the CP-type inverse filtering and its robustness with respect to the covariance frame position.