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.

2011-thesis-Approximate Bayesian Inference for Robust Speech Processing-Maina_PhD.pdf

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