Modeling and joint estimation of glottal source and vocal tract filter by state-space methods

Abstract

Accurate estimation of the glottal source from a voiced sound is a difficult blind separation problem in speech signal processing. In this work, state-space methods are investigated to enhance the joint estimation of the glottal source and the vocal tract information. The aim of this paper is twofold. First, a stochastic glottal source is proposed, based on deterministic Liljencrants–Fant model and ruled by a stochastic difference equation. Such a representation allows to accurately capture any perturbation occurring at glottal level in real voices. A state-space voice model is formulated considering the stochastic glottal source. Then, combining this voice model and the state-space framework, an inverse filtering method is developed that allows to jointly estimate both glottal source and vocal tract filter. The performance of this method is studied by means of experiments with voices synthesized by applying both the source-filter theory and a physical based voice model. The method is also test using human voice signals. The results demonstrate that accurate estimates of the glottal source and the vocal tract filter can be obtained over several scenarios. Moreover, the method is shown to be robust with respect to different phonation types.