文件名称:Approximate-Bayesian-Inference-for-Robust-Speech-
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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.
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.
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2011-thesis-Approximate Bayesian Inference for Robust Speech Processing-Maina_PhD.pdf