Particle methods for Bayesian modeling and enhancement of speech signals

J Vermaak, C Andrieu, A Doucet, SJ Godsill

Research output: Contribution to journalArticle (Academic Journal)peer-review

126 Citations (Scopus)


This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The main aim of the paper is to perform on-line estimation of the clean speech and model parameters and to determine the adequacy of the chosen statistical models. Efficient particle methods are developed to solve the optimal filtering and fixed-lag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the particle filter with frequentist methods. The modeling and enhancement performance of the models and estimation algorithms are evaluated in simulation studies on both synthetic and real speech data sets.
Translated title of the contributionParticle methods for Bayesian modeling and enhancement of speech signals
Original languageEnglish
Pages (from-to)173 - 185
Number of pages13
JournalIEEE Transactions on Speech and Audio Processing
Volume10 (3)
Publication statusPublished - Mar 2002

Bibliographical note

Publisher: Vermaak J (reprint author), Microsoft Res Ltd
Other identifier: IDS Number: 553DX


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