Joint stochastic-deterministic wiener filtering with recursive Bayesian estimation of deterministic speech

Stochastic-deterministic (SD) speech modelling exploits the predictability of speech components that may be regarded deterministic. This has recently been employed in speech enhancement resulting in an improved recovery of deterministic speech components, although the improvement achieved is largely dependant on how these components are estimated. In this paper we propose a joint SD Wiener filtering scheme that exploits the predictability of sinusoidal components in speech. Estimation of sinusoidal speech components is approached in the recursive Bayesian context, where the linearity of the joint SD Wiener filter and Gaussian assumptions suggest a Kalman filtering scheme for the estimation of sinusoidal components. A further refinement also imposes a restriction of a smooth spectral envelope on sinusoidal magnitude estimates. The resulting joint SD Wiener filtering scheme improves speech quality in terms of the perceptual evaluation of speech quality (PESQ) metric when compared to both the traditional Wiener filter and the proposed Wiener filter based on alternative estimates of deterministic speech components.