Joint Speech Enhancement and Speaker Identification Using Approximate Bayesian Inference

We present a variational Bayesian algorithm for joint speech enhancement and speaker identification that makes use of speaker dependent speech priors. Our work is built on the intuition that speaker dependent priors would work better than priors that attempt to capture global speech properties. We derive an iterative algorithm that exchanges information between the speech enhancement and speaker identification tasks. With cleaner speech we are able to make better identification decisions and with the speaker dependent priors we are able to improve speech enhancement performance. We present experimental results using the TIMIT data set which confirm the speech enhancement performance of the algorithm by measuring signal-to-noise (SNR) ratio improvement and perceptual quality improvement via the Perceptual Evaluation of Speech Quality (PESQ) score. We also demonstrate the ability of the algorithm to perform voice activity detection (VAD). The experimental results also demonstrate that speaker identification accuracy is improved.