Symmetric and asymmetric Gaussian weighted linear prediction for voice inverse filtering

Abstract

Weighted linear prediction (WLP) has demonstrated its significance in voice inverse filtering, contributing to enhanced methods for estimating both the vocal tract filter and the glottal source. WLP provides a mechanism to mitigate the effect on the linear prediction model of voice samples that affects the vocal tract filter estimation, particularly those samples around glottal closure instants (GCIs). This article studies the Gaussian weighted linear prediction (GLP) strategy, which employs a Gaussian attenuation window centered at the GCIs to reduce its contribution in the WLP analysis. In this study, the Gaussian attenuation is revisited and a parameterization of the window that adjusts to the typical variability in voice periodicity is introduced. In addition, an asymmetric Gaussian window is proposed to diminish the relevance of voice samples preceding GCIs on the WLP model, thus providing a quasi closed phase inverse filtering method. Characterization of symmetric and asymmetric GLP methods for glottal source estimation is addressed based on synthetic and natural phonation data, resulting in a set of optimal parameters for the Gaussian attenuation windows. The results show that the proposed asymmetric attenuation improves voice inverse filtering with respect to the symmetric GLP method. Comparisons with other state-of-the-art techniques suggest that the proposed GLP approaches are competitive, falling slightly short in performance only when contrasted with the well-known quasi closed inverse filtering analysis. The simplicity of implementing the attenuation windows, coupled with their robust performance, positions the proposed GLP methods as two attractive and straightforward voice inverse filtering techniques for practical application.