Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Particle Filtering for Non-Stationary Speech Modelling and Enhancement

Jaco Vermaak, Christophe Andrieu, Arnaud Doucet

Signal Processing Group, Department of Engineering, University of Cambridge, UK

This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The main aim of the pa- per is to perform on-line estimation of the clean speech and the model parameters, and to determine the adequacy of the chosen statistical models. An e∆cient simulation- based method is developed to solve the optimal filtering problem. The algorithm combines sequential importance sampling and a selection step, and employs several variance reduction strategies to make the best use of the statistical structure of the model. The modelling and enhancement performance of the model and algorithm are evaluated in simulation studies on real speech data sets.


Full Paper

Bibliographic reference.  Vermaak, Jaco / Andrieu, Christophe / Doucet, Arnaud (2000): "Particle filtering for non-stationary speech modelling and enhancement", In ICSLP-2000, vol.3, 594-597.