Approximate Bayesian robust speech processing

We present a comparison of two variational Bayesian algorithms for joint speech enhancement and speaker identification. In both algorithms we make use of speaker dependent speech priors which allows us to perform speech enhancement and speaker identification jointly. For the first algorithm we work in the time domain and in the second we work in the log spectral domain. Our work is built on the intuition that speaker dependent priors would work better than priors that attempt to capture global speech properties. Experimental results using the TIMIT data set are presented to demonstrate the speech enhancement and speaker identification performance of the algorithms. We also measure perceptual quality improvement via the PESQ score.