Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. These

parameters must often be estimated from noisy observations since speech signals are

rarely obtained in ‘clean’ acoustic environments in the real world. As a result, the

parameter estimation algorithms we employ must be robust to environmental factors

such as additive noise and reverberation. In this work we derive and evaluate approximate Bayesian algorithms for the following speech processing tasks: 1) speech

enhancement 2) speaker identification 3) speaker verification and 4) voice activity

detection.